Distributed computing for DNA analysis.
ABSTRACT We report on extensions to a Java distributed computation library (JDCL) by Fritsche, Power, and Waldron, with application to a problem in the field of bioinformatics. Within our framework the system has been extended to support applications requiring a MIMD (multiple instruction, multiple data) architecture. The system has been evaluated through a DNA pattern matching application over a network of 90 PCs. The user is required to extend only two Java classes to completely configure a distributed computation.
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ABSTRACT: This report describes the full design and development of a general-purpose programmable distributed environment. The aim of the system is to provide developers with a quick and easy platform for implementing distributed computations in the context of a MIMD architecture (multiple instruction, multiple data). The model underlying the system is a combination of the client-server model and the pipeline processor model. The design and implementation of the system is based on an early version of the Java Distributed Computation Library by Fritsche, Power, and Waldron. The distinguishing feature of the system is its ability to dynamically change the algorithm sent to clients. We have demonstrated the functionality of our system by solving a problem from the field of DNA analysis. Our system was evaluated over a local area network of approximately two hundred computers. This report contains a description of the JDCL and its modifications, a user manual, and the code for a sample distributed application.
Distributed computing for DNA analysis
T. Keane,1 R. Allen,1 T. J. Naughton,1 J. McInerney,2 and J. Waldron3
1 Department of Computer Science, National University of Ireland, Maynooth, Ireland
2 Department of Biology, National University of Ireland, Maynooth, Ireland
3 Department of Computer Science, Trinity College, Dublin 2, Ireland
Corresponding author: email@example.com
We report on extensions to a Java distributed computation library (JDCL) by Fritsche, Power,
and Waldron, with application to a problem in the field of bioinformatics. Within our
framework the system has been extended to support applications requiring a MIMD (multiple
instruction, multiple data) architecture. The system has been evaluated through a DNA pattern
matching application over a network of 90 PCs. The user is required to extend only two Java
classes to completely configure a distributed computation.
A class of distributed computation systems is based on the client-server model. This class is
characterised by (i) clients that instigate all communication and have no knowledge of each
other (no peer-to-peer communication), (ii) a server that has little information on, or control
of, its clients, and (iii) computations that are insensitive to fluctuations in the number of
clients or client failure. Well-known and successful systems in this class include the Great
Internet Mersenne Prime Search (GIMPS)  and SETI@Home . These systems are
usually designed with a single application in mind, and are not generalisable or
programmable. A Java distributed computation library (JDCL)  was designed to provide a
simple general-purpose platform for developers who wish to quickly implement a distributed
computation in the context of a SIMD (single instruction, multiple data) architecture. Its aims
were to allow developers to abstract completely from networking details and to allow
distributed computations to be reprogrammed without requiring any client-side
administration. Its attractions included network and platform independence, simplicity of
design, and ease of use for developers.
Our contribution has been to continue development of the system, bringing it to a level
in terms of functionality and robustness that permits demonstration of a large-scale
application. The JDCL was in an early stage of development and required a number of
enhancements to bring it up to such a level. In addition to refining the functionality and
efficiency of existing features of the JDCL  our system contains enhancements that are in
line with the aspirations of its original developers. They include facilitating ease of
distribution [the client consists of an initialisation file and a single jar (Java archive) file], and
coping with client failure. The server is capable of both detecting client failure and
redistributing the computational load.
Other enhancements (not aspirations of the original JDCL developers) include adding
security to the clients, and expanding the range of applications that the JDCL can support. A
security manager has been developed that limits the downloaded task’s interaction with the
client software and donor machine. The client software also integrates seamlessly with the
donor machines. As a low-priority service it utilises only ‘spare’ clock cycles so that the
inconvenience a donor experiences is minimised. The other major enhancement is the
system's emulation of a MIMD (multiple instruction, multiple data) architecture. This is
explained in Sect. 2. The design of the system is outlined in Sect. 3.
Java proved to be an ideal language for the development of this system. It was
possible to design a straightforward interface to the system: users are required to extend only
two classes to completely reconfigure a distributed computation. Furthermore, identical
clients (and identical downloaded tasks) could be run on a variety of platforms. Existing
programmable distributed environments or libraries range from MPI  and PVM  to
JavaSpaces  and the Java OO Neural Engine (Joone) . The strength of our system, we
believe, is that it takes full advantage of dynamic multiprocessor architectures (such as the
Internet itself) in which individual processors work in a completely asynchronous manner and
do not have the ability to efficiently support a shared memory.
2 Computational theory for MIMD emulation
A major enhancement of our system is its emulation of a MIMD architecture. In order to do
this, the server simulates a pipeline processor capable of repackaging and redistributing
partial results during a computation. In this section, we give the computational theory of
MIMD emulation through client server processing.
Consider an input X, and a computation on that input C(X) that returns some result r.
We could say that )(XCr =
. In client-server computing, the server partitions the input data
into n segments
such that each transformation
server reconstructs the original result by combining these partial results
is decomposed into m smaller transformations that each acts on the result of the previous
definition of this concept could be written as follows,
, 0 if)(
can be regarded as the seed to the recursion and defines the final result. The
first clause in Eq. (3) is the terminating condition (passing the input to the first
transformation) and the second clause describes how the result of any one transformation
depends on the preceding transformation. We use the following compact notation to represent
the recursive definition of Eq. (3),
where ∏ denotes the operation to appropriately pass the results of one transformation to
another. Equation (4) describes passing the complete input X to transformation c0, the result
being passed to c1, and so on. Staying within the pipeline processing paradigm, we could
further partition the input into n segments, as described in Eq. (1), and pass each segment in
turn through the complete sequence of m transformations. Appropriately combining the partial
results at the end of the final transformation, as in Eq. (2), would allow us to write Eq. (4) as
The advantages of the representation in Eq. (5) include the ability to arbitrarily change the
granularity of the data throughput (some transformations may have restrictions on the size or
format of their arguments) and to permit parallelisation of the computation. Pipeline
computations could possibly be regarded as MISD (multiple instruction, single data).
is performed by one of a set of clients. The
? denotes an appropriate combination operation. In pipeline processing, a computation
where X is the input. A recursive
; 0 if)(
It is possible to combine both the client-server (SIMD) and pipeline (MISD) models.
This is important if we want to allow clients to effect arbitrary transforms rather than each one
performing the same cj. In this case, the server divides the computation as well as the data. It
distributes to the clients a description of a transformation cj as well as a data segment xi. Since
the partitioning shown in Eq. (1) is possible, there will not be any interdependencies between
different parts of the data stream. Equations (4) and (2) could therefore be combined as
which describes transforming all of the data segments with cj before applying cj+1, and so on.
Since Eqs. (5) and (6) describe the same computation, this shows that the order in which each
cj(xi) is effected is unimportant, as long as one finds the appropriate (
order implementation of Eq. (6) is a MIMD computation. Consequently, an MIMD emulator
is the by-product of a loosely coupled client-server simulation of a highly structured pipeline
processor. This computational theory tells us nothing about how to find an appropriate (
pair, or how efficient the resulting MIMD emulation might be. Sanders  has proposed an
efficient algorithm to emulate MIMD computations on a synchronous SIMD system. Our
asynchronous system should admit emulation algorithms
that are even more efficient because it completely avoids
what Sanders calls SIMD overhead  (where the globally
issued instruction is not required locally). Our system is still
susceptible to load imbalance overhead but this problem-
dependent issue is inherent to all parallel computing,
including MIMD parallelism. Figure 1 shows an abstract
model of the system. The user sees a pipeline processor,
which sits on a client-server architecture and uses the JDCL.
The standard client-server model is available to the user by constructing a pipeline composed
of a single processing stage.
? ,∏) pair. An out-of-
3 Design of the system
The design mirrors that of Sanders  with a number of enhancements inspired by our
computational model. The user partitions the MIMD algorithm into multiple independent
sequential stages, if possible. Each stage corresponds to a node in a theoretical ‘pipeline.’ The
code corresponding to all stages (the Task) is sent to clients as a Java class. Execution of each
of the (one or more) stages then proceeds as a SIMD computation as in . The server
maintains a list (or Bucket) of partial results corresponding to the input to each pipeline stage.
As soon as there are sufficient partial results in a bucket (as specified by the user) they are
combined by the server and sent to a client along with a token indicating which part of the
code to execute (i.e. which stage in the pipeline). The client returns this token so that the
server can file the result in the next Bucket in the sequence. As such, all stages of the pipeline
could be ‘processing’ at the same time if the particular problem allowed. Our system is
therefore most efficient at emulating MIMD computations that can be naturally expressed as a
pipeline of SIMD computations.
The server is divided into three main sections. The ServerEngine (see Fig. 2) manages
all server-side data structures, classes, and logs. It retrieves all of its parameters from a user-
defined initialisation file and is responsible for loading the user-defined classes. The
communication section (ConnectionManager, etc.) handles all the communication between
the server and client. The user-defined classes (Task and DataHandler) are extended by the
user to specify a distributed computation. DataHandler partitions the data to be bundled with
copies of Task, sets appropriate flags (if necessary) in Task, and collates the partial results.
The Bucket class is available to the user to avail of pipeline processor functionality when
Fig. 1: System layers of abstraction
designing DataHandler. The client design (see Fig. 3) can be split into two main parts. The
ClientEngine is responsible for such tasks as initialising the software, starting the security
manager, log files, and GUI. The ClientMessageHandler is responsible for tasks such as
implementing and managing the communications protocol, initialising and managing the
downloaded Task, keeping track of all timing functions, and managing the data units from the
server. Full details on the design of the JDCL and its extensions can be found in [3,9].
It is evident that when simulating a pipeline processor the server is responsible for effecting
peer-to-peer communication between neighbouring processing stages, and will undoubtedly
be the bottleneck in a MIMD emulation. In fact, finite bandwidth at the server (rather than
finite memory) ultimately limits the scalability of the system. This is because the server
maintains information only about tasks (completed and uncompleted): space complexity is
dependent only on the size of the computation and is independent of the number of clients.
Since the server is designed never to initiate communication with a client, an essential
practical consideration is sharing the limited bandwidth at the server between clients returning
results and clients looking for a new task. This balance is investigated while examining how
the server copes with overloading through an empirical evaluation.
A cut-down version of the DNA substring problem (outlined in Sect. 5) was
partitioned into 100 work units (each requiring approximately 6 minutes of processing time),
and repeatedly solved over different numbers of clients in the range [1,90]. We employed a
laboratory of 90 Dell Optiplex GX1 machines (Pentium 600MHz processor, 128Mbytes
memory, 6Gbytes storage). Our server resided on a Dell Optiplex GX110 machine (Pentium
1000MHz processor, 256Mbytes memory) with a 10Mbit/s connection to the laboratory. For
these tests we had sole use of the processor and network resources. Clients were encoded with
two wait times. A ‘retry wait’ determines how long a client will wait before retrying to
connect to the server (if it failed to connect). A ‘null wait’ determines how long a client will
wait before asking for a new task after the server previously informed it that there were
currently no outstanding tasks. The smaller the null wait time the quicker clients will come on
board when the server does need them. However, unsuccessful requests for tasks could slow
the overall computation by blocking clients that are trying to return results.
To simulate in the order of thousands of client connections we handicapped the server
at an operating system level by only allowing it a fixed number of socket connections each
minute. As the allowable rate of socket connections was reduced, we found that the system’s
performance became increasingly sensitive to the difference between the null wait time and
the processing time required for the task. Figure 4(a) shows plots of processing time against
number of clients, for two different null wait times, a fixed retry wait of 10s, and a maximum
rate of 15 connections/minute at the server. Figure 4(b) shows the same data plotted to
indicate speedup. The plots show that by carefully selecting the null wait time we can strike a
balance between pressing clients into service as soon as possible and blocking clients that are
trying to return results with requests for new tasks. [Figure 4(b) shows a comparison with the
desired linear speedup. Our deviation from this can be partially explained by not configuring
the number of tasks to be very much greater than the number of processors.]
Strands of DNA can be regarded as strings of base-4 symbols. The nucleotides adenine,
guanine, cytosine, and thymine are represented by the symbols A, G, C, and T, respectively.
Our application involved building up a picture of the repeated substrings within a DNA
strand. We chose the DNA of the tuberculosis bacterium, which contains approximately 5M
nucleotides. As well as exact-matched substrings, we also permitted insertions and deletions,
up to a maximum in each case, because slightly different DNA strings can code for the same
functionality. In each case, we searched the complete DNA strand and recorded the locations
of all repeated substrings of length greater than 13 in a database. The longest exact-match
substring in tuberculosis contains 1526 nucleotides and first appears at index 400211 of its
sequenced DNA. A more detailed account of these results is in preparation .
We ran the three distributed algorithms over the aforementioned laboratory of 90
clients and recorded the speedup data shown in Table 1. For these computations we did not
have sole use of the laboratory. The number of processors varied as at times some machines
were switched off or were booted into operating systems on which a JVM was not installed.
We noted, however, that at all times at least 40 processors were working for the server. The
disparity between speedups (i) and (ii) was due to choosing a task size for the former that was
too small (thus not making efficient usage of the intra-laboratory network resources). The
difference between speedups (ii) and (iii), we believe, simply reflects the uncertainty of
resource-availability in a busy university laboratory environment. Taking only the results for
insertions and deletions, our system has demonstrated an average speedup of 53 with
(assuming a full complement of 90 processors) an efficiency of 59%.
We have refined the JDCL in terms of efficiency and functionality, including the successful
extension of the system to emulate a MIMD architecture. This has allowed us to implement a
large-scale bioinformatics application. The system is completely generalisable, and because it
is written in Java, the developer interface can be simplified to the extension of two classes. As
would be expected, the system is also platform and network independent. Future work
includes a scheduler for the server and a selection of client-side configuration options to
increase its acceptability among potential donors.
We gratefully acknowledge assistance from the Department of Computer Science,
NUI Maynooth, and technicians M. Monaghan, P. Marshall, and J. Cotter. We also thank the
anonymous reviewers of this paper for their valuable contributions.
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Fig. 2. Server design: the user extends Task (which is sent to the client) and DataHandler.
Fig. 3. Client design.
Number of processors
Null wait 5 min
Null wait 15 min
100 units @ ~6 min, retry wait 10s
Fig. 4. Evaluation of overloading on the server: (a) processing time, and (b) speedup.
(i) Exact matching
Table 1. Speedup achieved for each of the three repeated substring search strategies.