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Runtime Code Generation with JVM And CLR

Authors:

Abstract

Modern bytecode execution environments with optimizing just-in-time compilers, such as Sun's Hotspot Java Virtual Machine, IBM's Java Virtual Machine, and Microsoft's Common Language Runtime, provide an infrastructure for generating fast code at runtime. Such runtime code generation can be used for efficient implementation of parametrized algorithms. More generally, with runtime code generation one can introduce an additional binding-time without performance loss. This permits improved performance and improved static correctness guarantees.
Runtime Code Generation with
JVM and CLR
Peter Sestoft (sestoft@dina.kvl.dk)
Department of Mathematics and Physics
Royal Veterinary and Agricultural University, Copenhagen, Denmark
and
IT University of Copenhagen
Draft version 1.00 of 2002-10-30
Unpublished material. All rights reserved.
Abstract Modern bytecode execution environments with optimizing just-in-time compilers, such as Sun’s
Hotspot Java Virtual Machine, IBM’s Java Virtual Machine, and Microsoft’s Common Language Runtime,
provide an infrastructure for generating fast code at runtime. Such runtime code generation can be used for
efficient implementation of parametrized algorithms. More generally, with runtime code generation one can
introduce an additional binding-time without performance loss. This permits improved performance and
improved static correctness guarantees.
We report on several experiments with runtime code generation on modern execution platforms. In
particular, we show how to introduce C#-style delegates in Java using runtime code generation, to avoid
most of the overhead of wrapping and unwrapping method arguments and method results usually incurred
by reflective method calls. Furthermore, we give a high-speed implementation of the Advanced Encryption
Standard (AES, also known as Rijndael) in C# using runtime code generation. Finally, we experiment with
sparse matrix multiplication using runtime code generation on both platforms.
1 Introduction
Runtime code generation has been a research topic for many years, but its practical use has been somewhat
limited. Traditionally, runtime code generation tools have been bound not only to a particular language, but
also to a particular platform. For instance, runtime code generation for C must take into account the ma-
chine architecture, operating system conventions, and compiler-specific issues such as calling conventions.
Abstracting away from these details often results in sub-optimal code.
To avoid this platform dependence, runtime code generation has often been done in bytecode execution
systems, but then the generated code will not be executed at native speed. This makes it much harder to
achieve worthwhile speed-ups.
New bytecode-based execution platforms such as the Java Virtual Machine and Microsoft’s Common
Language Runtime (CLR) contain just-in-time compilers that generate well-optimized native machine code
from the bytecode at runtime. Thus, these platforms combine the advantages of bytecode generation (ease
of code generation, portability) with native code generation (speed of the generated code).
Moreover, both platforms incorporate a bytecode verifier that detects type errors and other flaws before
executing the bytecode. While the resulting error messages are not particularly detailed (‘Operation may
corrupt the runtime’) they are more useful than a program crash that happens billions of instructions after
the runtime was corrupted by flawed code generated at runtime.
1
1.1 The Java Virtual Machine (JVM)
The Java Virtual Machine (JVM) is a specification of a stack-based object-oriented bytecode language.
Implementations such as Sun Microsystem’s Hotspot Java Virtual Machine
1
[6, 29] and IBM’s J2RE Java
Virtual Machine
2
[13] execute JVM bytecode by a mixture of interpretation, just-in-time (JIT) generation
of native machine code, and adaptive optimizations. There are two versions of the Sun Hotspot JVM, the
Client VM, which generates reasonable code fast, and the Server VM, which generates more optimized code
more slowly by performing dead code elimination, array bounds check elimination, and so on.
The Java class library does not provide facilities for runtime code generation, but several third-party
packages exist, for instance Bytecode Engineering Library BCEL [1] and
gnu.bytecode
[5].
Our experiments suggest that bytecode generated at runtime is usually compiled to machine code that is
just as efficient as bytecode compiled from Java source code. So a priori there is no performance penalty
for generating bytecode at runtime instead of compiletime (from Java source code). For instance, consider
a simple loop such as this:
do {
n--;
} while (n != 0);
The corresponding JVM bytecode may be:
0: iinc 0 -1 // Decrement local variable number 0 (n) by 1
iload_0 // Load local variable number 0 (n)
ifne 0 // Go to instruction 0 if variable was non-zero
The same loop can be expressed like this, using general bytecode instructions for subtraction, duplication,
and comparison:
0: iload_0 // Load local variable number 0 (n)
iconst_1 // Push constant 1
isub // Subtract
dup // Duplicate result
istore_0 // Store in local variable number 0 (n)
iconst_0 // Push constant 0
if_icmpgt 0 // Go to instruction 0 if n > 0
Although the latter code sequence is twice as long, the just-in-time compiler generates equally fast machine
code, and this code is just as fast as code compiled from the Java loop shown above.
Sun’s Hotspot Client VM performs approximately 240 million iterations per second, and the IBM JVM
performs more than 400 million iterations per second. In both cases this is just as fast as bytecode compiled
from Java source, and in case of the IBM JVM, as fast as optimized code compiled from C; see Figure 1.
This shows that bytecode generated at runtime carries no inherent speed penalty compared to code compiled
from Java programs. The Sun Hotspot Server VM optimizes more aggressively, and removes the entire loop
because it is dead code.
On the same platform, straightline JVM bytecode can be generated at a rate of about 200,000 bytecode
instructions per second with Sun HotSpot Client VM and 180,000 bytecode instructions per second with
the IBM JVM, using the
gnu.bytecode
package [5]; the BCEL package [1] is only half as fast. The code
generation time includes the time taken by the just-in-time compiler to generate machine code.
1
We use Sun HotSpot Client VM and Server VM 1.4.0 under Linux on an 850 MHz Mobile Pentium 3.
2
We use IBM J2RE JVM 1.3.1 under Linux on an 850 MHz Mobile Pentium 3.
2
Sun HotSpot IBM MS C
Client Server JVM CLR gcc -O2
Compiled loop (million iter/sec) 243 421 408 422
Generated loop (million iter/sec) 243 421 408 N/A
Code generation (thousand instr/sec) 200 142 180 100 N/A
Figure 1: Code speed for simple loop, and code generation speed.
In the Java Virtual Machine, the size of a method body is limited to 65535 bytes of bytecode. It is
usually pointless to generate methods larger than that at runtime, but this limitation sometimes complicates
experiments.
The Sun HotSpot JVM and the IBM JVM are available for free for the operating systems Linux, Mi-
crosoft Windows, Solaris, HP-UX, Apple MacOS X, IBM AIX, and IBM OS/390, and for a range of pro-
cessor types.
Other implementations of the JVM with just-in-time compilation are available, but we have not tested
their suitability for runtime code generation.
Some notable JVM implementations suitable for experimentation are IBM’s Jikes Research Virtual Ma-
chine (RVM, formerly Jalapeño) in which a range of dynamic optimizations may be turned on or off [14],
and Intel’s Open Platform Runtime [4].
1.2 The Microsoft Common Language Runtime (CLR)
Another major bytecode execution platform is Microsoft’s Common Language Runtime (CLR), part of the
so-called .Net platform. Like the Java Virtual Machine it uses a stack-based object-oriented bytecode, and
nativelysupports runtime code generation using the .Net Frameworkclassesin namespace
System.Reflect.Emit
.
Experiments confirm that CLR bytecode generated at runtime is no less efficient than bytecode compiled
from C#. Under Microsoft’sCLR
3
, bytecode generated at runtime performs more than 400 million iterations
per second in the simple
do-while
loop shown in the preceding section, which is just as fast as code
compiled from C#. The Microsoft CLR just-in-time compiler appears to generate code that is comparable
in speed to that of the IBM JVM and somewhat faster than Sun HotSpot Client VM; see Figure 1. The
experiment reported in Section 4 shows that this expectation scales to more realistic programs also. Probably
we underestimate the real of MS CLR performance slightly, because we run MS Windows under VmWare,
not on the bare machine.
The Microsoft CLR seems more sensitive than the JVMs to the actual choice of bytecode instructions.
For instance, loop bytecode that involves
dup
operations on the evaluation stack performs only 250 million
iterations per second, 37 percent less than the first bytecode sequence shown in the preceding section.
Multiple load instructions are clearly preferable to a load followed by a
dup
. Moreover, the speed shows
large but reproducible variations depending on what other code can actually be executed (later) by the
program.
Straightline CLR bytecode can be generated at a rate of about 100,000 bytecode instructions per second,
including the time used by the just-in-time compiler to generate x86 instructions from the bytecode. Most of
the code generation time (around 95 per cent) is spent in the just-in-time compiler, but the exact proportion
probably depends on the composition of the code.
So far, CLR runtime code generation facilities are available only from Microsoft and for MS Windows
and FreeBSD, but as the Mono project [3] develops, they will become available for a range of platforms.
3
We use Microsoft CLR version 1.0 SP2 under Windows 2000 under VmWare 3.0 under Linux with an 850 MHz Mobile
Pentium 3.
3
1.3 Related work
Runtime code generation has been used for many years in the Lisp, Scheme, and Smalltalk communities.
The backquote and comma notation of Lisp and Scheme originates from MIT Lisp Machine Lisp (1978),
and may be inspired by W.v.O. Quine’s quasiquotation (1960). Backquote and comma are classical tools
for code generation, which together with an
eval
or
compile
function provides a means for runtime code
generation.
A number of technical reports by Keppel, Eggers and Henry argued the utility of runtime code generation
also in other programming languages [25, 26]. Later work in the same group led to the DyC system for
runtime code generation in C [8, 22].
Engler and others [18] developed very fast tools for runtime code generation in C. An untyped two-level
language ‘C (tick-C) was based on the Vcode library [19]. Neither Vcode nor ‘C is maintained any longer
(2002). The Gnu Lightning library [2] may be considered a replacement for Vcode; it currently supports
portable runtime code generation in C for the Intel x86, Sun Sparc, and PowerPC processor architectures.
Leone and Lee developed Fabius, a system for runtime code generation in Standard ML, implemented
in the SML/NJ system [27]. It is no longer maintained.
The Tempo specializer from Consel’s group is a partial evaluator for C that can generate specialized
programs at runtime, generating machine code using so-called code templates [12].
Bytecode generation tools for Java include
gnu.bytecode
[5], developed for the Kawa implementation
of Scheme, and the Bytecode Engineering Library (BCEL) [1], formerly called JavaClass, which is used in
several projects.
Oiwa, Masuhara and Yonezawa [31] present a Java-based typed two-level language for expressing dy-
namic code generation in Java, rather than using JVM bytecode. The code generation speed and the runtime
results reported in that paper are however somewhat disappointing.
The Microsoft .Net Framework Library uses runtime code generation, for instance in the implementa-
tion of regular expressions in namespace
System.Text.RegularExpressions
. The source code of this
implementation is available as part of the shared source implementation of the CLR (also known as Rotor).
Cisternino and Kennedy developed a library for C# that considerably simplifies runtime generation of
CIL code [11]. It uses C# custom attributes and requires a modification of the CLR.
At least two libraries for runtime bytecode generation in Caml bytecode exist, namely Rhiger’s bytecode
combinators [32], and Lomov and Moskal’s Dynamic Caml [28]. Dynamic Caml permits runtime code
generating programs to be written using a two-level source language syntax, so one does not have to work
at the bytecode level.
MetaML [33] and MetaOCaml [10] are typed multi-level languages based on Standard ML and OCaml.
The first type system for a multi-stage language was the two-level lambda calculus proposed by Nielson
[30]. Davies and Pfenning generalized Nielson’s type system, relating it to the modal logic S4. An extension
of this type system was used by Wickline, Lee and Pfenning in a typed multi-stage version of ML [34].
Runtime code generation is related to generative programming [15], staged computation [24] and partial
evaluation [23]. Just-in-time compilation and dynamic compilation (as in the Sun HotSpot JVMs and Mi-
crosoft’s CLR) are themselves instances of runtime code generation. Machine code is generated just before
it is to be executed, usually from a higher-level bytecode language, and possibly taking into account infor-
mation available only at runtime, such as method call patterns, the actual CPU type (AMD Athlon or Intel
Pentium 2, 3 or 4), and so on.
The implementation of delegates for efficient reflectivemethod calls in Java shown in Section 3 is related
to Breuel’s work on implementing dynamic language features in Java [9]. That paper hints at the possibility
of creating delegates in analogy to other constructions in the paper. However, the Dynamic toolkit described
in Breuel’s paper does not appear to be available.
4
2 Examples of runtime code generation
This section shows a few examples of runtime code generation for Microsoft’s CLR using C#.
2.1 Evaluation of polynomials
Consider the evaluation of a polynomial p x for many different values of x. The polynomial p x of degree
n in one variable x and with coefficent array cs is defined as follows:
p x cs 0 cs 1 x cs 2 x
2
cs n x
n
According to Horner’s rule, this formula is equivalent to:
p x cs 0 x cs 1 x x cs n x 0
Therefore p x can be computed by this
for
-loop, which evaluates the above expression inside-out and
stores the result in variable
res
,
double res = 0.0;
for (int i=cs.Length-1; i>=0; i--)
res = res * x + cs[i];
return res;
If we need to evaluate a polynomial with fixed coefficents cs for a large number of different values of x,
then it may be worthwhile to unroll this loop to a sequence of assignments, where
cs_i
is the contents of
array cell cs
i :
double res = 0.0;
res = res * x + cs_n;
...
res = res * x + cs_1;
res = res * x + cs_0;
return res;
These source statements could be implemented by stack-oriented bytecode such as this:
Ldc_R8 0.0 // push res = 0.0 on stack
Ldarg_1 // load x
Mul // compute res * x
Ldc_R8 cs_n // load cs[n]
Add // compute res * x + cs[n]
...
Ldarg_1 // load x
Mul // compute res * x
Ldc_R8 cs_0 // load cs[0]
Add // compute res * x + cs[0]
Return // return res
This bytecode can be generated at runtime using classes from the namespace
System.Reflection.Emit
in
the CLR Framework, as follows:
5
ilg.Emit(OpCodes.Ldc_R8, 0.0); // push res = 0.0 on stack
for (int i=cs.Length-1; i>=0; i--) {
ilg.Emit(OpCodes.Ldarg_1); // load x
ilg.Emit(OpCodes.Mul); // compute res * x
ilg.Emit(OpCodes.Ldc_R8, cs[i]); // load cs[i]
ilg.Emit(OpCodes.Add); // compute res * x + cs[i]
}
ilg.Emit(OpCodes.Ret); // return res;
The
ilg
variable holds a bytecode (Intermediate Language) generator, representing a method body to be
generated. Executing
ilg.Emit(...)
appends a bytecode instruction to the instruction stream. The
Ldc_R8
instruction loads a floating-point constant onto the evaluation stack. The
Ldarg_1
instruction loads method
parameter number 1, assumed to hold x, onto the stack. The
Mul
instruction multiplies the two top-most
stack elements, and the
Add
instruction adds the two top-most stack elements, leaving the result on the stack
top.
The generated code will be a linear sequence of instructions for pushing a constant or variable, or for
multiplying or adding stack elements; it contains no loops, tests, or array accesses. All loop tests and array
accesses are performed at code generation time.
As a further optimization, when a coefficient cs i is zero, pushing and adding it has no effect and no
code needs to be generated for it. The code generator is easily modified to perform this optimization:
ilg.Emit(OpCodes.Ldc_R8, 0.0); // push res = 0.0 on stack
for (int i=cs.Length-1; i>=0; i--) {
ilg.Emit(OpCodes.Ldarg_1); // load x
ilg.Emit(OpCodes.Mul); // compute res * x
if (cs[i] != 0.0) {
ilg.Emit(OpCodes.Ldc_R8, cs[i]); // load cs[i]
ilg.Emit(OpCodes.Add); // compute x * res + cs[i]
}
}
ilg.Emit(OpCodes.Ret); // return res;
Runtime code generation is interesting in the polynomial example because there are two binding-times in
the input data. The coefficient array cs is available early, whereas the value of the variable x is available
only later.
Another way of saying this is that cs remains fixed over a large number of different values of x, so
specialization of the general loop code with respect to cs
is worthwhile. This permits staging: In stage
one, the coefficient array cs is available, and all computations depending only that array are performed. In
stage two, the computations depending also on x are performed.
Staging by runtime code generation gives a speed-up over the straightforward implementation already
for polynomials of degree n higher than 4, and especially if some coefficients cs i are zero. Speed-up factors
of 2 to 4 appear to be typical.
6
2.2 The power function
For another example, consider computing x
n
, that is, x raised to the n’th power, for integers n 0 and
arbitrary x. This example is a classic in the partial evaluation literature. The C# or Java function
Power(n,
x)
below computes x
n
:
public static int Power(int n, int x) {
int p;
p = 1;
while (n > 0) {
if (n % 2 == 0)
{ x = x * x; n = n / 2; }
else
{ p = p * x; n = n - 1; }
}
return p;
}
The function relies on these equivalences, for n even (n 2m) and n odd (n 2m 1):
x
2m
x
2
m
x
2m
1
x
2m
x
Note that the
while
- and
if
-conditions in
Power
depend only on n, so for a given value of n one can unroll
the
while
-loop and eliminate the
if
-statement. Thus if we have a fixed value of n and need to compute
x
n
for many different values of x, we can generate a specialized function
power_n(x)
that takes only one
parameter x and avoids all the tests and computations on n.
The following C# method
PowerGen
takes n as argument and generates the body of a
power_n
instance
method, using a bytecode generator
ilg
for the method being generated. The
PowerGen
method performs
the computations that depend only on n, and generates code that will later perform the computations involv-
ing x and p:
public static void PowerGen(ILGenerator ilg, int n) {
ilg.DeclareLocal(typeof(int)); // declare p as local_0
ilg.Emit(OpCodes.Ldc_I4_1);
ilg.Emit(OpCodes.Stloc_0); // p = 1;
while (n > 0) {
if (n % 2 == 0) {
ilg.Emit(OpCodes.Ldarg_1); // x is arg_1
ilg.Emit(OpCodes.Ldarg_1);
ilg.Emit(OpCodes.Mul);
ilg.Emit(OpCodes.Starg_S, 1); // x = x * x
n = n / 2;
} else {
ilg.Emit(OpCodes.Ldloc_0);
ilg.Emit(OpCodes.Ldarg_1);
ilg.Emit(OpCodes.Mul);
ilg.Emit(OpCodes.Stloc_0); // p = p * x;
n = n - 1;
}
}
ilg.Emit(OpCodes.Ldloc_0);
ilg.Emit(OpCodes.Ret); // return p;
}
7
Note that the structure of
PowerGen
is very similar to that of
Power
. The main difference is that operations
that depend on the late or dynamic argument x have been replaced by actions that generate bytecode, whereas
operations that depend only on the early or static argument n are executed as before. Roughly,
PowerGen
could be obtained from
Power
just by keeping all code that can be executed using only early or static
information (variable n), and replacing the remaining code by bytecode-generating instructions.
A method such as
PowerGen
is called the generating extension of
Power
. Given a value for
Power
s
static argument n, it generates a version of
Power
specialized for that value.
For n 16, the specialized method generated by
PowerGen
is considerably faster than the general
method
Power
; see Figure 2. The fastest way to call the generated specialized method is to use an in-
terface call; a reflective call is very slow, on the other hand. The same pattern is seen in Java. Delegate calls
(in C#) are twice as slow as interface calls, apparently.
In a Java implementation of this example, calling the specialized method can be 4 times faster than
calling the general method when using the Sun HotSpot Client VM, approximately 8 times faster when
using the Sun HotSpot Server VM, and fully 22 times faster with the IBM JVM. This very high speed-up
factor is probably achieved by inlining the specialized method code during machine code generation, so
similar speed-ups cannot be expected when larger or more complex code is generated.
Sun HotSpot IBM MS
Client Server JVM CLR
Reflective call to specialized (n 16) 8.225 2.041 24.235 26.879
Interface call to specialized (n 16) 1.334 0.094 0.026 0.260
Delegate call to specialized (n 16) N/A N/A N/A 0.541
Static call to general method
5.369 0.759 0.711 0.711
Interface call to general method 5.677 0.830 0.571 0.741
Delegate call to general method N/A N/A N/A 1.062
Figure 2: Time in seconds for 10 million calls to specialized and general power methods.
2.3 Practical CLR bytecode generation
In the examples above we have focused on generating the bytecode for a method body. Here we see how to
set up the context for that bytecode, and how to execute it. The goal is to generate some method
MyMethod
in some class
MyClass
, as if declared by
class MyClass : IMyInterface {
public MyClass() : base() { }
public double MyMethod(double x) {
... method body generated using ilg.Emit(...) ...
}
...
}
The method’s body consists of the bytecode generated using
ilg
. To be able to call the generated method
MyMethod
efficiently,
MyClass
must implement an interface
IMyInterface
that describes the method. The
idea is that we can create an instance of the class, cast it to the interface, and then call the method
MyMethod
on that instance. For this to work, the interface must describe the generated method:
8
interface IMyInterface {
double MyMethod(double x);
}
Moreover, the class must have a constructor
MyClass()
as shown above, so that one can create instances of
the class. The constructor’s only action is to call the superclass constructor.
In the CLR, a class must belong to some module, and a module must belong to an assembly. Hence
before one can generate any code, one needs an
AssemblyBuilder
, a
ModuleBuilder
, a
TypeBuilder
(for the class), and a
MethodBuilder
, as outlined below. The
ILGenerator ilg
is then obtained from the
MethodBuilder
, with the purpose of generating the method’s body:
AssemblyName assemblyName = new AssemblyName();
AssemblyBuilder assemblyBuilder = ... assemblyName ...
ModuleBuilder moduleBuilder = assemblyBuilder.DefineDynamicModule(...);
TypeBuilder typeBuilder = moduleBuilder.DefineType("MyClass", ...);
... (1) generate a constructor in class MyClass ...
MethodBuilder methodBuilder = typeBuilder.DefineMethod("MyMethod", ...);
ILGenerator ilg = methodBuilder.GetILGenerator();
... (2) use ilg to generate the body of method MyClass.MyMethod ...
The class must have a constructor so that we can make an instance of it. An argumentless constructor that
simply calls the superclass (
Object
) constructor can be built using this boilerplate code:
ConstructorBuilder constructorBuilder =
typeBuilder.DefineConstructor(MethodAttributes.Public,
CallingConventions.HasThis,
new Type[] { });
ILGenerator ilg = constructorBuilder.GetILGenerator();
ilg.Emit(OpCodes.Ldarg_0); // push the current object, ‘this’
ilg.Emit(OpCodes.Call, typeof(Object).GetConstructor(new Type[] {}));
ilg.Emit(OpCodes.Ret);
After the method body has been generated, the class must be created by a call to method
CreateType
:
Type ty = typeBuilder.CreateType();
The object bound to
ty
represents the newly built class
MyClass
, containing the instance method
MyMethod
.
To call the method, create an instance
obj
of the class, cast it to the interface
IMyInterface
, and call the
method in that object. To create an instance of the class we obtain the class’s argumentless constructor and
call it using the reflection facilities of CLR:
Object obj = ty.GetConstructor(new Type[] {}).Invoke(new Object[] { });
IMyInterface myMethod = (IMyInterface)obj;
double res = myMethod.MyMethod(3.14);
The interface method call is fast because no argument wrapping or result unwrapping is needed: the argu-
ment 3.14 is passed straight to the generated bytecode.
Alternatively, one can use the CLR reflection facilities to get a handle
m
to the generated method by
evaluating the expression
ty.GetMethod("MyMethod")
. Using the handle, one can then call the method,
passing arguments in an object array, and getting the results as an object:
9
MethodInfo m = ty.GetMethod("MyMethod");
double res = (double)m.Invoke(null, new object[] { 3.14 });
However, reflective calls to a method handle are inefficient because of the need to wrap the method’s ar-
guments as an object array, and similarly unwrap the method result. This wrapping and unwrapping is
syntactically implicit in C#, but takes time and space even so.
A third way to call the generated method is to turn it into a so-called delegate (a typed function ref-
erence); this avoids the argument wrapping and result unwrapping but still turns out to be slower than the
interface method call. A delegate such as
myMethod
can be called directly without any wrapping or unwrap-
ping of values:
D2D myMethod = (D2D)Delegate.CreateDelegate(typeof(D2D),
obj,
ty.GetMethod("MyMethod"));
double res = myMethod(3.14);
The example above assumes that the delegate type
D2D
describes a function that takes a
double
argument x
and returns a
double
result. The delegate type
D2D
may be declared as follows in C#:
public delegate double D2D(double x);
Other delegate (function) types may be described similarly.
2.4 Practical Java bytecode generation with gnu.bytecode
Here we show the necessary setup for generating a Java class
MyClass
containing a method
MyMethod
, using
the
gnu.bytecode
package [5]. An object
co
representing a named class must be created, with specified
superclass and access modifiers. As in Section 2.3 it must also be declared to implement an interface
describing the generated method:
ClassType co = new ClassType("MyClass");
co.setSuper("java.lang.Object");
co.setModifiers(Access.PUBLIC);
co.setInterfaces(new ClassType[] { new ClassType("IMyInterface") });
... (1) generate a constructor in class MyClass ...
Method mo = co.addMethod("MyMethod");
mo.setSignature("(D)D");
mo.setModifiers(Access.PUBLIC);
mo.initCode();
CodeAttr jvmg = mo.getCode();
... (2) use jvmg to generate the body of method MyClass.MyMethod ...
An argumentless constructor is added to class
MyClass
(step 1 above) by adding a method with the spe-
cial name
<init>
. The constructor should just call the superclass constructor, using an
invokespecial
instruction:
10
Method initMethod =
co.addMethod("<init>", new Type[] {}, Type.void_type, 0);
initMethod.setModifiers(Access.PUBLIC);
initMethod.initCode();
CodeAttr jvmg = initMethod.getCode();
Scope scope = initMethod.pushScope();
Variable thisVar = scope.addVariable(jvmg, co, "this");
jvmg.emitLoad(thisVar);
jvmg.emitInvokeSpecial(ClassType.make("java.lang.Object")
.getMethod("<init>", new Type[] {}));
initMethod.popScope();
jvmg.emitReturn();
Then a method represented by method object
mo
must be added to the class (step 2 above), with given
signature and access modifiers, and a code generator
jvmg
is obtained for the method and is used to generate
the method body. An example use of code generators in
gnu.bytecode
can be seen in Section 5.2.
Once the constructor and the method have been generated, a representation of the class is written to a
byte array and loaded into the JVM using a class loader. This produces a class reference
ty
representing the
new class
MyClass
:
byte[] classFile = co.writeToArray();
Class ty = new ArrayClassLoader().loadClass("MyClass", classFile);
An instance
obj
of the class is created and cast to the interface describing the generated method, and then
the method in the object is called using an interface call:
Object obj = ty.newInstance();
IMyInterface myMethod = (IMyInterface)obj;
double res = IMyInterface.MyMethod(3.14);
Alternatively, one can use reflection on
ty
to obtain an object
m
representing the method in the class, and
then call that method:
java.lang.reflect.Method m =
ty.getMethod("MyMethod", new Class[] { double.class });
Double ro = (Double)m.invoke(obj, new Object[] { new Double(3.14) });
double res = ro.doubleValue();
As can be seen, this requires wrapping of arguments and unwrapping of results, which is costly. Recall that
in Java,
double.class
is an object that represents the primitive type
double
at runtime. As in C#, reflective
method calls are slow because of the wrapping and unwrapping, but in Java there is no built-in way to avoid
them.
2.5 Practical Java bytecode generation with BCEL
The Bytecode Engineering Library BCEL [1] is another third-party Java library that can be used for runtime
code generation. Here we outline the necessary setup for code generation with BCEL.
One must create a class generator
cg
, specifying superclass, access modifiers and a constant pool
cp
for
the generated class, and an interface describing the generated method. Then one must generate a constructor
for the class, and the method:
11
ClassGen cg = new ClassGen("MyClass", "java.lang.Object",
"<generated>",
Constants.ACC_PUBLIC | Constants.ACC_SUPER,
new String[] { "Int2Int" });
InstructionFactory factory = new InstructionFactory(cg);
ConstantPoolGen cp = cg.getConstantPool();
... (1) generate a constructor in class MyClass ...
InstructionList il = new InstructionList();
MethodGen mg = new MethodGen(Constants.ACC_PUBLIC | Constants.ACC_STATIC,
Type.DOUBLE, new Type[] { Type.DOUBLE },
new String[] { "x" },
"MyMethod",
"MyClass",
il, cp);
... (2) use il to generate the body of method MyClass.MyMethod ...
mg.setMaxStack();
cg.addMethod(mg.getMethod());
An argumentless constructor is added to class
MyClass
(step 1 above) by adding a method with the special
name
<init>
. The constructor should just call the superclass constructor:
InstructionFactory factory = new InstructionFactory(cg);
InstructionList ilc = new InstructionList();
MethodGen mgc = new MethodGen(Constants.ACC_PUBLIC,
Type.VOID,
new Type[] { }, new String[] { },
"<init>", "MyClass",
ilc, cp);
ilc.append(factory.createLoad(Type.OBJECT, 0));
ilc.append(factory.createInvoke("java.lang.Object", "<init>",
Type.VOID,
new Type[] { },
Constants.INVOKESPECIAL));
ilc.append(new RETURN());
mgc.setMaxStack();
cg.addMethod(mgc.getMethod());
When a method body has been generated (step 2 above), we can write a representation
clazz
of the class to
a byte array and then load it into the JVM using a class loader. This gives a class reference
ty
representing
the new class
MyClass
:
JavaClass clazz = cg.getJavaClass();
byte[] classFile = clazz.getBytes();
Class ty = new ArrayClassLoader().loadClass("MyClass", classFile);
As in Section 2.4, an instance of the class is created and cast to the interface describing the generated
method, and then the method in the object is called using an interface call:
Object obj = ty.newInstance();
IMyInterface myMethod = (IMyInterface)obj;
double res = IMyInterface.MyMethod(3.14);
12
3 Efficient reflective method calls in Java
This section shows that runtime code generation can be used to introduce the concept of a delegate (typed
function reference) in the Java programming language. Delegates are known from C# and other program-
ming languages. The experiments below show that delegates can be used to improve the speed of Java
reflective method calls by a factor of 8 to 16.
This is primarily of interest when a pre-existing method needs to be called by reflection several thousand
times. A method generated at runtime should be called by interface calls through an interface implemented
by the generated class to which the method belongs, as shown in Sections 2.3 through 2.5. This is far more
efficient than a reflective call, and also more efficient than a delegate call as implemented here.
3.1 Reflective method calls are slow
In Java one can obtain an object
mo
of class
java.lang.reflect.Method
representing a named method
from a named class, and one can call that method using a reflective method call
mo.invoke(...)
. However,
the arguments to method
mo
must be given as an array of objects, and the result is returned as an object;
this means that primitive type arguments must be wrapped as objects, and primitive type results must be
unwrapped.
This wrapping of arguments and unwrapping of results imposes a considerable overhead on reflective
method calls. A reflective call to a public method in a public class appears to be 16 to 28 times slower than
a direct call. See Figure 3. The slowdown is higher for methods taking arguments than for those that do not,
because of the argument wrapping and unwrapping. The reflective slowdown is approximately the same
regardless of whether the called method is static, virtual, or called via an interface. A reflective call to a
public method in a non-public class is slower by a further factor of 8, probably due to access checks on the
class.
Static method Instance method
Reflective Direct Reflective Virtual Interface
No arguments or results 3.279 0.288 3.39 0.329 0.372
Integer argument and result 7.746 0.310 8.18 0.346 0.343
Figure 3: Time in seconds for 10 million method calls, using Sun HotSpot Client VM.
The figure shows that reflective method calls carry a considerable performance overhead. In Microsoft’s
CLR, most of this overhead can be avoided by turning the reflective method reference into a delegate (Sec-
tion 2.3). Java does not have a built-in notion of delegate, but the next sections show how to add delegates
to the Java programming language using runtime code generation. This way, most of the call overhead can
be avoided.
3.2 Delegate objects in Java
To implement delegates, we need an interface describing the delegate by a method
invoke
with appropriate
argument and result types. We generate a new class that implements that interface and whose
invoke
method calls the method obtained by reflection, and then we create an instance
dlg
of the generated class
and cast it to the interface. A call to
dlg.invoke
will call the method obtained by reflection, yet without
the overhead of wrapping and unwrapping.
Consider a method
m
from a class
C
with return type
R
and parameter types
T1
, ...,
Tn
. Assume for now
that the method is static; our technique works also for instance methods (Section 3.4):
13
public class C {
public static R m(T1 x1, ..., Tn xn) { ... }
...
}
Using Java’s reflection facilities one can obtain a method object
mo
of class
java.lang.reflect.Method
,
and then one can call method
m
by invoking
mo.invoke(wrargs)
, where
wrargs
is an array of objects
holding the argument values
v1
, ...,
vn
. Thus any arguments of primitive type (
int
,
double
, and so on)
must be wrapped as objects. The call will return the method’s result (if any) as an object, which must be
cast to
m
s result type
R
, or unwrapped in case
R
is a primitive type.
Instead of calling
m
via the method reference
mo
, with wrapping and unwrapping of arguments, we
shall create and use a delegate object
dlg
. The delegate object will be made to implement a user-declared
interface
OI
that describes a method
invoke
with precisely the parameter types and result types of
m
, so
dlg.invoke
and
m
have the same signature:
interface OI {
R invoke(T1 x1, ..., Tn xn);
}
Then one can call
m(v1, ..., vn)
as
dlg.invoke(v1, ..., vn)
via the delegate
dlg
, without any wrap-
ping of the argument values
v1
, ...,
vn
, nor unwrapping of results.
The implementation of this idea is detailed in the sections below. The efficiency of the approach is
illustrated in Figure 4, which should be read as additional columns for Figure 3 above. We see that a
delegate call to a method without arguments is 8 times faster than an ordinary reflective call. The speed-
up factor is closer to 16 for a method with arguments, because the reflective call’s argument wrapping is
avoided in the delegate call. A call via a delegate is still around 50 percent slower than a direct call, but this
slowdown is quite similar to that incurred by delegate calls in CLR.
Static method Instance method
Delegate Delegate
No arguments or results 0.446 0.477
Integer argument and result 0.487 0.537
Figure 4: Time in seconds for 10 million delegate calls, using Sun HotSpot Client VM.
The speed-up derives from two sources: (1) access permissions and types are checked once and for all
when the delegate is created, not at every call; and (2) the object allocation required by argument wrapping
and the downcasts implied by result unwrapping are avoided completely. With the IBM JVM, reflective
calls are 3 times slower than with Sun HotSpot Client VM, but delegate calls are 10 times slower. Thus with
the IBM JVM, delegate calls are only 3 to 5 times faster than reflective calls.
Note the importance of the user-declared interface
OI
. It enables the compiler to statically typecheck
every call
dlg.invoke(...)
to the delegate, although the delegate object
dlg
, and the class of which it
is an instance, will be created only at runtime. Our delegate implementation makes sure (and the bytecode
verifier checks) that the delegate actually implements the interface
OI
.
3.3 The implementation of delegate creation
Our implementation of delegate objects in Java uses the
gnu.bytecode
[5] package, but could use the BCEL
[1] package instead. Assume as above that
mo
is a
java.lang.reflect.Method
object referring to a static
14
method
m
in a class
C
:
public class C {
public static R m(T1 x1, ..., Tn xn) { ... }
...
}
Assume further that there is an interface
OI
describing a method
invoke
with the same (unwrapped) return
type and parameter types as
m
:
interface OI {
R invoke(T1 x1, ..., Tn xn);
}
We create, at runtime, a new class
Dlg
that implements
OI
, and whose
invoke
method simply calls the real
method
C.m
represented by
mo
and returns its result, if any. In Java syntax, the class
Dlg
would look like
this (assuming that
m
s return type
R
is non-
void
):
public class Dlg implements OI extends Object {
public Dlg() { super(); }
public R invoke(T1 p1, ..., Tn pn) {
return C.m(p1, ..., pn);
}
}
The new class
Dlg
(which is generated by the bytecode tools, not in Java source form) is loaded into the Java
Virtual Machine, and an instance
dlg
of class
Dlg
is created; this is the delegate. By construction,
dlg
can
be cast to the interface
OI
, and calling
dlg.invoke(...)
has the same effect as calling
mo.invoke(...)
,
but avoids the overhead of wrapping and result unwrapping arguments and result. The only overhead is an
additional interface method call.
We implement delegate creation by a static method
createDelegate
. A delegate
dlg
that corresponds
to method object
mo
and implements interface
OI
can be created and called as follows:
OI dlg = (OI)(Delegate.createDelegate(OI.class, mo));
... dlg.invoke(v1, ..., vn) ...
Note the cast to interface
OI
, and recall that in Java,
OI.class
is an object of class
java.lang.Class
representing the interface type
OI
at runtime.
More precisely, our implementation of
createDelegate(iface, mo)
performs the following steps:
1. Check that
iface
represents a public interface that has a single method
invoke
.
2. Check that the
invoke
method in the interface has the same parameter and result types as the method
represented by
mo
.
3. Check that
mo
represents a public static method
m
(or a public instance method, see Section 3.4) in a
public classs
C
.
4. Create a
gnu.bytecode.ClassType
object representing a new public class
Dlg
that implements the
interface represented by
iface
.
15
5. Generate a public argumentless constructor
Dlg()
and add it to the class.
6. Generate a public method
invoke
and add it to the class. The method has the same parameter and
result types as
invoke
in the interface
iface
.
7. Generate JVM bytecode for
invoke
s body. The code contains instructions to call method
C.m
with
precisely the arguments passed to
invoke
, and ends with a return instruction of the appropriate kind.
8. Write a representation of the new class
Dlg
to a byte array.
9. Invoke a classloader to load the
Dlg
class from the byte array into the JVM. The JVM bytecode
verifier will check the new class before loading it.
10. Use reflection to create an object
dlg
of class
Dlg
, and return that object as the result of the call to
createDelegate(iface, mo)
.
The checks make sure that the generated bytecode passes the bytecode verifier.
Experiments indicate that the time to create a
Dlg
class and a delegate using this approach is approx-
imately 1.5 ms (with the Sun HotSpot Client VM). Thus a delegate must be called approximately 2000
times before the cost of creating the delegate has been recovered. Not surprisingly, this prototype imple-
mentation of delegate creation in Java is approximately 100 times slower than the built-in delegate creation
in Microsoft’s CLR.
Creating a
Dlg
class and a delegate as above consumes approximately 1100 bytes of memory in the Sun
HotSpot Client VM. However, a separate
Dlg
class needs to be created only for each distinct method
C.m
.
Hence the
createDelegate
method should cache
Dlg
classes (using a
HashMap
, for instance) and reuse
them when creating new delegates for a method already seen. This is especially important when creating
delegates over instance methods (see below), where many delegates may be created from the same method.
3.4 Implementing delegate objects for instance methods
The previous section described the implementation of delegates for static methods. The procedure to create
a delegate for an instance method
m
is almost the same, only the creation of the delegate must store a receiver
object in the delegate object
dlg
.
Assume we have a method object
mo
representing an instance method
m
of class
C
, with return type
R
and parameter types
T1
, ...,
Tn
:
class C {
public R m(T1 x1, ..., Tn xn) { ... }
...
}
Also assume that the (receiver) object
obj
is an object of class
C
or one of its subclasses. The programmer
must declare an interface
OI
describing a method
invoke
with the same argument types and result type as
m
, precisely as in Section 3.3.
Now the call
createDelegate(OI.class, mo, obj)
should return an object
dlg
that can be cast to
interface
OI
, and so that a call to
dlg.invoke(...)
will call the method as if by
obj.mo(...)
.
This works as above, except that the class
Dlg
constructed at runtime must contain a reference to the
receiver object
obj
of class
C
, and must use it when invoking
m
, so the class
Dlg
would look like this in Java
syntax:
16
public class Dlg implements I extends Object {
private C obj;
public Dlg() { super(); }
public void setObj(Object obj) {
this.obj = (C)obj;
}
public R invoke(T1 p1, ..., Tn pn) {
return obj.m(p1, ..., pn);
}
}
The new class
Dlg
is loaded into the JVM using a class loader, an instance
dlg
of the class is created,
and the
dlg.setObj(obj)
method is called on the given receiver object
obj
. Now
dlg
is the delegate for
obj.m(...)
and is returned as the result of the call to
Delegate.createDelegate(...)
.
Instead of using a
setObj
method, one might create a one-argument constructor in class
Dlg
and pass
obj
when creating the delegate object. This would complicate the class generator slightly.
17
4 Efficient implementation of the Advanced Encryption Standard
The Advanced Encryption Standard (AES) [7], also known as the Rijndael algorithm after its inventors J.
Daemen and V. Rijmen, is the US Federal standard for encryption of sensitive (unclassified) information.
It succeeds the DES encryption algorithm and is expected to be adopted also by the private sector, and
internationally.
Starting from an efficient baseline implementation of AES in C#, we have made an implementation that
uses runtime code generation in CLR to create a specialized block encryption/decryption routine for a given
key. The same bytecode generator generates both the encryption routine and the decryption routine. It works
for all three standard key sizes (128, 192, 265 bit), and a block size of 128 bit.
The specialized code generated at runtime (for a known key) is 35 percent faster than the best we could
write by hand (for a fixed key size, but unknown key). The generated code can encrypt or decrypt approxi-
mately 139 Mbit/s under the Microsoft CLR. Extrapolating from data givenby Rijmen and Daemen, a highly
optimized implementation by Brian Gladman using Visual C++ and native Pentium 3 rotate instructions (not
expressible in ANSI C nor in C#) can encrypt 280–300 Mbit/s [16, 21]. Given that our implementation runs
in a managed execution environment, its performance is wholly satisfactory.
4.1 Brief description of the AES
The AES algorithm is a block cipher, that is, it encrypts data in blocks of 128 bits (16 bytes) at a time. The
algorithm works in two phases:
(1) Given an encryption or decryption key (of size 128, 192 or 256 bits), create an array
rk[]
of so-called
round keys. Each round key can be considered a 4 by 4 block of bytes. The number of rounds (
ROUNDS
= 10, 12, or 14) depends on the size of the key.
(2) For each 128 bit data block
d
to encrypt, perform the following operations:
(2.1) Add first round key
rk[0]
to the data block:
KeyAddition(d, rk[0])
The
KeyAddition
is performed by xor’ing the round key into the data block
d
, byte-wise.
(2.2) Perform the following operations for each intermediate round
r = 1, ..., ROUNDS-1
:
Substitution(d, S)
ShiftRow(d)
MixColumn(d)
KeyAddition(d, rk[r])
The
Substitution
step replaces each byte
b
in the data block by
S[b]
, where
S
is a so-called
S
-box, a 256-entry table of bytes. The
S
-box represents an invertible affine mapping, composed
of a polynomial multiplication modulo x
8
1 followed by an addition.
The
ShiftRow
operation rotates the rows of the data block
d
left by 0, 1, 2, or 3 bytes.
The
MixColumn
operation transforms each column of the data block
d
, using polynomial multi-
plication modulo x
4
1.
Finally, the
KeyAddition
xors the key
rk[r]
into the data block
d
, byte-wise.
(2.3) The last round, for which
r = ROUNDS
, has no
MixColumn
operation, and therefore consists of
these steps:
18
Substitution(d, S)
ShiftRow(d)
KeyAddition(d, rk[r])
Decryption can be performed simply by doing these steps in reverse, using the inverse
Si
of the
S
-box,
inverse
MixColumn
, and so on. However, by simple algebraic properties of the algorithm, decryption can be
performed by a sequence of steps very similar to that for encryption, using the round keys backwards after
applying inverse
MixColumn
to the round keys
rk[1]
through
rk[ROUNDS-1]
.
4.2 Implementing AES in C#
The sequence (2) of operations must be performed for each data block to be encrypted or decrypted. It
can be implemented efficiently on architectures with 32-bit words and sufficient memory, as described in
Daemen and Rijmen’s AES Proposal paper [16], and implemented in Cryptix’s Java implementation of AES
[20].
We have followed this approach, which requires auxiliary tables
T0
,
T1
,
T2
,
T3
to be built as 256-
entry tables of unsigned 32-bit integers, each representing the composed action of the
Substitution
and
MixColumn
operations. Then the intermediate rounds (step 2.2) of encryption can be implemented using
bitwise operations on unsigned 32-bit integers, or 4 bytes in parallel. In C# it can be done like this:
for(int r = 1; r < ROUNDS; r++) {
k = rk[r];
uint t0 =
T0[a0 >> 24] ^
T1[(a1 >> 16) & 0xFF] ^
T2[(a2 >> 8) & 0xFF] ^
T3[a3 & 0xFF] ^ k[0];
uint t1 =
T0[a1 >> 24] ^
T1[(a2 >> 16) & 0xFF] ^
T2[(a3 >> 8) & 0xFF] ^
T3[a0 & 0xFF] ^ k[1];
uint t2 =
T0[a2 >> 24] ^
T1[(a3 >> 16) & 0xFF] ^
T2[(a0 >> 8) & 0xFF] ^
T3[a1 & 0xFF] ^ k[2];
uint t3 =
T0[a3 >> 24] ^
T1[(a0 >> 16) & 0xFF] ^
T2[(a1 >> 8) & 0xFF] ^
T3[a2 & 0xFF] ^ k[3];
a0 = t0; a1 = t1; a2 = t2; a3 = t3;
}
Above,
k[0]
is the first column of the round key, as a 32-bit unsigned integer,
k[1]
is the second column,
and so on.
19
4.3 A deoptimized implementation of AES
In fact, the AES middle round implementation shown above has been hand-optimized already. The most
compact and general implementation is shown below, where 4-element arrays
a[]
,
t[]
and
T[]
are used
instead of the variables
a
,
t
and
T
above:
for(int r = 1; r < ROUNDS; r++) {
k = rk[r];
uint[] t = new uint[4];
for (int j = 0; j < 4; j++) {
uint res = k[j];
for (int i = 0; i < 4; i++)
res ^= T[i][(a[(i + j) % 4] >> (24 - 8 * i)) & 0xFF];
t[j] = res;
}
a[0] = t[0]; a[1] = t[1]; a[2] = t[2]; a[3] = t[3];
}
For given
i
and
j
in the range 0 3, the indexing into
T[i]
with
a[j]
can be uniformly expressed as
T[i][(a[(i+j)%4 >> (24-8*i)) & 0xFF]
.However, it is tempting to write the complicated hand-optimized
form in Section 4.2 because of the presumed efficiency of hand-specializing for
i
and
j
and indeed the
resulting implementation is 5 times faster. Below we shall see that runtime code generation allows us to
write the general algorithm and obtain the efficiency of the specialized one, and more.
Moreover, when
i
is 0 or 3, the shift count is 24 or 8, in which case the indexexpression can be simplified
by eliminating the bitwise ‘and’ (
&
) or the shift, because
aj
is a 32-bit unsigned integer.
The first round (step 2.1, not shown) is still just a key addition (using the C# xor operator
^
), and the
last round (step 2.3, not shown) uses the
S
box bytewise, since it involves no
MixColumn
. Decryption can be
implemented by a similar sequence of operations, as hinted above, but when handwriting the code one will
usually specialize it for efficiency, so that some of the similarity with the encryption algorithm is lost.
4.4 Runtime generation of encryption code
Using runtime code generation, one can unroll the step 2.2
for
-loop shown above, and inline the round keys
rk[r]
. Since no computations can be performed on the basis of the round key alone, one would think that
this gives no speed-up at all. However, apparently the just-in-time compiler in Microsoft’s CLR can perform
more optimizations on the unrolled code: it is around 35 per cent faster when encrypting or decrypting a
large number of data blocks.
The three nested
for
-loops below generate code corresponding to an unrolling of the
for r
-loop shown
in Section 4.2:
20
for (int r = 1; r < ROUNDS; r++) {
k = rk[r];
for (int j = 0; j < 4; j++) {
ilg.Emit(OpCodes.Ldc_I4, k[j]); // Push k[j]
for (int i = 0; i < 4; i++) {
ilg.Emit(OpCodes.Ldloc, T[i]);
ilg.Emit(OpCodes.Ldloc, a[encrypt ? (i+j) % 4 : (j+4-i) % 4]);
if (i != 3) {
ilg.Emit(OpCodes.Ldc_I4, 24 - 8 * i);
ilg.Emit(OpCodes.Shr_Un);
}
if (i != 0) {
ilg.Emit(OpCodes.Ldc_I4, 0xFF);
ilg.Emit(OpCodes.And);
}
ilg.Emit(OpCodes.Ldelem_U4);
ilg.Emit(OpCodes.Xor);
}
ilg.Emit(OpCodes.Stloc, t[j]); // Assign to tj
}
for (int j = 0; j < 4; j++) { // Generate a0=t0; a1=t1; ...
ilg.Emit(OpCodes.Ldloc, t[j]);
ilg.Emit(OpCodes.Stloc, a[j]);
}
}
Above,
k[j]
is the
j
’th column of the round key
k = rk[r]
. The variable
T[i]
holds the code generator’s
representation of a local variable corresponding to table
Ti
for
i
in 0 3; variable
a[j]
holds the represen-
tation of local variable
aj
; and variable
t[j]
holds the representation of local variable
tj
. The variable
encrypt
determines whether code is generated for encryption (true) or decryption (false); the tables
Ti
for
encryption and decryption are different also.
One iteration of the inner
for
-loop generates code to compute
Ti[(aj >> 24-8*i) & 0xFF]
and xor
it into the value on the stack top. The
if
-statements in the inner
for
-loop implement the optimization for
i
being 0 or 3, discussed in Section 4.3. Writing this logic in the code generator is actually simpler and less
error-prone than hand-writing the optimized code it generates (Section 4.2).
One iteration of the middle
for
-loop generates code to compute the right-hand side in the initialization
uint ti = ...
from Section 4.2, and to assign it to
ti
. One iteration of the outer
for
-loop generates the
code corresponding to one iteration of the
for
-loop in Section 4.2.
4.5 Pragmatic considerations
Approximately 256 bytes of MSIL bytecode is generated for each round of encryption, or roughly 2600
bytes of bytecode for the entire encryption function (when the key size is 128 bit). It is unclear how much
x86 code is generated by the CLR just-in-time compiler from this bytecode.
Two questions are central to the practical viability of this approach to encryption:
The encryption key equals the first round key
rk[0]
and therefore can be recovered from the generated
code. Hence the generated code should not be cached anywhere on disk, and should not be accessible
to other applications running in the same operating system.
Since a specialized routine is generated for each key, it would be useful to be able to discard the
generated code when it is no longer in use.
21
5 Sparse matrix multiplication
A plain implementation of the multiplication R A B of two n n matrices uses n
3
scalar multiplications.
When matrix B has only few non-zero elements (say, 5 percent), matrix multiplication can profitably be
performed in two stages: (1) make a list of all non-zero elements in each column of B, and (2) to compute
element R
ij
of R, multiply the elements of row i of A only with the the non-zero elements of column j of
B. With 100 100 matrices and 5 percent non-zero elements, this is approximately 2 times faster than plain
matrix multiplication.
With runtime code generation, there is a further possibility, especially interesting if A
B must be com-
puted for many different As and a fixed sparse matrix B. Namely, split step (2) above into: (2a) for every j,
generate code to compute R
ij
for fixed j, and then (2b) use that code to compute R
ij
for every i. Still using
100 100 matrices with 5 percent non-zero elements, this can be a further 4 to 10 times faster than sparse
matrix multiplication, or 8 to 20 times faster than plain matrix multiplication.
5.1 Matrix multiplication and sparse matrix multiplication
Plain matrix multiplication R A B, assuming that the matrices are non-empty rectangular arrays of appro-
priate sizes, can be implemented as follows:
final int aCols = A[0].length, rRows = R.length, rCols = R[0].length;
for (int i=0; i<rRows; i++)
for (int j=0; j<rCols; j++) {
double sum = 0.0;
for (int k=0; k<aCols; k++)
sum += A[i][k] * B[k][j];
R[i][j] = sum;
}
Sparse matrix multiplication can be implemented as shown below. We assume that the
SparseMatrix
object
computed for B has a method
getCol(j)
that returns a list of the non-zero elements of the j’th column of
the matrix. Each non-zero element
nz
is represented by its row
nz.k
and its value
nz.Bkj
.
SparseMatrix sparseB = new SparseMatrix(B);
final int rRows = R.length, rCols = R[0].length;
for (int i=0; i<rRows; i++) {
final double[] Ai = A[i];
final double[] Ri = R[i];
for (int j=0; j<rCols; j++) {
double sum = 0.0;
Iterator iter = sparseB.getCol(j).iterator();
while (iter.hasNext()) {
final NonZero nz = (NonZero)iter.next();
sum += Ai[nz.k] * nz.Bkj;
}
Ri[j] = sum;
}
}
Note that multiplication happens in two stages as suggested above: (1) first compute the
sparseB
represen-
tation of B, then (2) multiply that with the non-sparse representation of A.
22
5.2 Generating a sparse multiplication routine
We now further split the second stage into two. In stage (2a) we generate code for the second stage, special-
ized with respect to
sparseB
,unroll the
for j
-loop and the
while
-loop, but keep the
for i
-loop. Unrolling
the
for i
-loop would make only a few more operations static, and would make the generated code much
larger.
The bytecode generation (stage 2a) can be implemented like this, assuming that
sparseB
is given:
Label loop = new Label(jvmg);
loop.define(jvmg); // do {
jvmg.emitLoad(varA);
jvmg.emitLoad(vari);
jvmg.emitArrayLoad(double1D_type);
jvmg.emitStore(varAi); // Ai = A[i]
jvmg.emitLoad(varR);
jvmg.emitLoad(vari);
jvmg.emitArrayLoad(double1D_type);
jvmg.emitStore(varRi); // Ri = R[i]
for (int j=0; j<B.cols; j++) {
jvmg.emitLoad(varRi); // Load Ri
jvmg.emitPushInt(j);
jvmg.emitPushDouble(0.0); // sum = 0.0
Iterator iter = B.getCol(j).iterator();
while (iter.hasNext()) {
final NonZero nz = (NonZero)iter.next();
jvmg.emitPushDouble(nz.Bkj); // load B[k][j]
jvmg.emitLoad(varAi); // load A[i]
jvmg.emitPushInt(nz.k);
jvmg.emitArrayLoad(Type.double_type); // load A[i][k]
jvmg.emitMul(); // prod = A[i][k]*B[k][j]
jvmg.emitAdd(’D’); // sum += prod
}
jvmg.emitArrayStore(Type.double_type); // R[i][j] = sum
}
jvmg.emitLoad(vari);
jvmg.emitPushInt(1);
jvmg.emitAdd(’I’);
jvmg.emitStore(vari); // i++
jvmg.emitLoad(vari);
jvmg.emitPushInt(aRows);
jvmg.emitGotoIfLt(loop); // } while (i<aRows);
jvmg.emitReturn();
Above we assume that the generated code’s parameters A and R are held in generation-time variables
varA
and
varR
, and similarly for the generated code’s variables
Ai
,
Ri
, and
i
.
The
i
-loop is expressed as a
do-while
loop in the generated code. The generated loop begins with the
label
loop
, and ends with the conditional jump instruction generated by
emitGotoIfLt(loop)
. The use of
a
do-while
loop is consistent with the assumption that the matrices are non-empty.
After the code has been generated, we use it in stage (2b) to compute all cells of the resulting matrix R.
23
5.3 Experimental results
We have implemented this idea in Java using the
gnu.bytecode
[5] bytecode generation package; see
Section 2.4. Runtime generation of a sparse matrix multiplication routine specialized for B can produce
code that is 4 to 10 times faster than a general sparse matrix multiplication routine, and 8 to 20 times faster
than plain matrix multiplication.
These runtime figures are for the Sun HotSpot Client VM. The Sun HotSpot Server VM is faster for
bytecode compiled from Java code, but (in this case) slower for bytecode generated at runtime. It is unclear
why 1000 matrix multiplications take more than 15 times as long as 100 matrix multiplications in Sun
HotSpot Server VM. The IBM JVM is even more variable, in that runtime code generation gives a net
slowdown of 25 percent when performing 100 matrix multiplications, but a speed-up by a factor of 6 when
performing 1000 matrix multiplications. This may be due to dynamic optimizations performed on frequently
executed code. See Figure 5.
100 matrix multiplications 1000 matrix multiplications
Sun HotSpot IBM MS Sun HotSpot IBM MS
Client Server JVM CLR Client Server JVM CLR
Plain 2.749 2.302 1.067 1.432 27.489 21.890 10.535 14.230
Sparse 1.118 0.820 0.904 1.191 10.660 5.548 7.405 11.567
Sparse, 2-phase
0.993 0.458 0.684 0.931 9.814 4.438 6.737 9.263
Sparse, rtcg 0.222 0.467 1.341 0.300 0.920 6.995 1.482 0.691
Figure 5: Time in seconds for multiplications of 100 100 sparse (5 percent) matrices.
The expected number of instructions in the specialized multiplication method for 100 100 sparse (5
percent) matrices is 100 5 15 bytes, that is, 37.5 KB. This seems to translate into 60 KB of generated
assembly code and other overhead when compiled by the just-in-time compiler in Sun HotSpot Client VM,
and 70 KB in MS CLR; but in general we have no accurate way to estimate this number. The time to
generate a specialized sparse multiplication method is roughly 15 ms with Sun HotSpot Client VM, and
roughly 260 ms with MS CLR. For Sun HotSpot Client VM, runtime code generation pays off after only
two uses of the generated function, whereas 40 uses are required on the MS CLR platform.
24
6 Conclusion
We have demonstrated that runtime code generation is well supported by modern execution platforms such
as Sun’s HotSpot JVM, IBM’s JVM, and Microsoft’s Common Language Runtime. This is due primarily
to:
the simplicity of generating stack-oriented portable bytecode,
the support from bytecode verifiers, and
highly optimizing just-in-time native code generators.
Together these features make portable runtime code generation fairly easy and safe, and the generated code
efficient. We demonstrated this using several small examples, and two slightly larger ones.
We have found the Microsoft CLR runtime code generation facilities to be well-documented and well
integrated with the reflection facilities.
For the Java Virtual Machine one needs to use third-party code generation libraries. The
gnu.bytecode
runtime code generation facilities are rather poorly documented, but they are well-designed and therefore
fairly easy to use. The BCEL runtime code generation facilities are fairly well-documented, but offers many
ways to do the same thing, and may be somewhat bewildering for this reason.
First, we have shown how to implement delegates (as known from C#) in Java using runtime code
generation. Using delegates one can avoid some of the inefficiency usually incurred by reflective method
calls. This works better with the Sun HotSpot Client VM than with the IBM JVM.
Secondly, we have shown that the Advanced Encryption Standard (AES, Rijndael) algorithm can be
implemented efficiently in C# using runtime code generation, reaching speeds otherwise not possible on the
managed Common Language Runtime platform.
Third, we have shown that runtime code generation can be profitably applied also to sparse matrix
multiplication, at least when the matrices are large enough and sparse enough.
The execution platforms (Sun HotSpot Client JVM, Sun HotSpot Server JVM, IBM JVM, and Mi-
crosoft’s CLR) display quite diverging execution speeds for different types of programs. For instance, the
IBM JVM executes bytecode compiled from Java program faster than Sun HotSpot Client VM does, but is
considerably slower when executing reflective method calls and when executing delegates as implemented
in Section 3. The Sun HotSpot Server VM appear to represent a middle point between these. The Microsoft
CLR is comparable to IBM JVM when executing bytecode compiled from Java, and executes code generated
at runtime very fast, too.
In general, the execution times of JIT-compiled code are subject to considerable variations, probably
because of the dynamic and somewhat heuristic nature of the optimizations performed by the JIT-compiler.
Often, the execution times exhibit strange but reproducible variations between very similar bytecode pro-
grams.
Moreover, the hardware platform (e.g. Intel Pentium 3 versus AMD Athlon) affects code speed in non-
obvious ways. In particular, the speed-up factor obtained by runtime code generation can be very different
on the two architectures.
We have focused mainly on technological aspects of runtime code generation. To make runtime code
generation more practical and safer, research could focus on these aspects:
Language mechanisms that permit the programmer to work at the Java or C# level instead of the
bytecode level. Previous work in this direction is represented by Tick C [19], and MetaML [33]
MetaOCaml [10]. The DynJava implementation [31], and Cisternino and Kennedy’s C# toolkit [11]
seem to be the only attempts in this direction for Java and C#.
25
A code generation framework or type system that could guarantee at Java or C# compiletime that
the generated bytecode will pass the verifier. Although the JVM or CLR verifier will catch all code
errors, it is far better to have a static guarantee that the generated code will be verifiable. Previous
relevant work includes that by Davies and Pfenning [17], and the type systems of MetaOCaml [10]
and DynJava [31].
Acknowledgements: Martin Elsman suggested looking at encryption algorithms for a case study, and
providedcomments on a draft. Thanks also to Kasper Østerbye, Ken Friis Larsen, and Niels JørgenKokholm
for pointers and suggestions.
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28
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We present the design of DyC, a dynamic-compilation system for C based on run-time specialization. Directed by a few declarative user annotations that specify the variables and code on which dynamic compilation should take place, a binding-time analysis computes the set of run-time constants at each program point in the annotated procedure's control-flow graph; the analysis supports program-point-specific polyvariant division and specialization. The results of the analysis guide the construction of a run-time specializer for each dynamically compiled region; the specializer supports various caching strategies for managing dynamically generated code and mixes of speculative and demand-driven specialization of dynamic branch successors. Most of the key cost/benefit trade-offs in the binding-time analysis and the run-time specializer are open to user control through declarative policy annotations.DyC has been implemented in the context of an optimizing compiler, and initial results have been promising. The speedups we have obtained are good, and the dynamic-compilation overhead is among the lowest of any dynamic-compilation system, typically 20–200 cycles per instruction generated on a Digital Alpha 21164. The majority of DyC's functionality has been used to dynamically compile an instruction-set simulator. Only three annotations were required, but a few other changes to the program had to be made due to DyC's lack of support for static global variables. This deficiency and DyC's rudimentary support for partially static data structures are the primary obstacles to making DyC easy to use.
Conference Paper
Functional languages have proven substantially useful for hosting embedded domain-specific languages. They provide an infrastructure rich enough to define both a convenient syntax for the embedded language, a type system for embedded programs, and an evaluation mechanism for embedded programs. However, all existing host languages either interpret embedded programs instead of compiling them or require an expensive pre-compilation phase. In this article we close this gap in an implementation of the functional language OCaml: We provide a library of OCamlb yte-code combinators that is reminiscent of quasi-quotation in Lisp and of ’C and that enables just-in-time compilation of embedded programs. We illustrate these byte-code combinators on a prototypical domain-specific language.