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Offloading C++17 Parallel STL on System Shared Virtual Memory Platforms
Abstract
Shared virtual memory simplifies heterogeneous platform programming by enabling sharing of memory address pointers between heterogeneous devices in the platform. The most advanced implementations present a coherent view of memory to the programmer over the whole virtual address space of the process. From the point of view of data accesses, this System SVM (SSVM) enables the same programming paradigm in heterogeneous platforms as found in homogeneous platforms. C++ revision 17 adds its first features for explicit parallelism through its "Parallel Standard Template Library" (PSTL). This paper discusses the technical issues in offloading PSTL on heterogeneous platforms supporting SSVM and presents a working GCC-based proof-of-concept implementation. Initial benchmarking of the implementation on an AMD Carrizo platform shows speedups from 1.28X to 12.78X in comparison to host-only sequential STL execution.
... As detailed in the programming models approach, there are many projects aiming at highlevel parallel programming in C ++ , although two major blocks can be distinguished to provide abstraction to the heterogeneous system and facilitate programmability. There are those that offer an STL-like API [372,[416][417][418], sometimes being applied as backends of other technologies [373,419,420], increasing the efficiency for modern runtimes. And on the other side, common approaches to provide abstraction are the pattern-based proposals and algorithmic skeletons [377,379,380,382,414,421,422]. ...
Heterogeneous systems are becoming increasingly relevant, due to their performance and energy efficiency capabilities, being present in all types of computing platforms, from embedded devices and servers to HPC nodes in large data centers. Their complexity implies that they are usually used under the task paradigm and the host-device programming model. This strongly penalizes accelerator utilization and system energy consumption, as well as making it difficult to adapt applications.
Co-execution allows all devices to simultaneously compute the same problem, cooperating to consume less time and energy. However, programmers must handle all device management, workload distribution and code portability between systems, significantly complicating their programming.
This thesis offers contributions to improve performance and energy efficiency in these massively parallel systems. The proposals address the following generally conflicting objectives: usability and programmability are improved, while ensuring enhanced system abstraction and extensibility, and at the same time performance, scalability and energy efficiency are increased. To achieve this, two runtime systems with completely different approaches are proposed.
EngineCL, focused on OpenCL and with a high-level API, provides an extensible modular system and favors maximum compatibility between all types of devices. Its versatility allows it to be adapted to environments for which it was not originally designed, including applications with time-constrained executions or molecular dynamics HPC simulators, such as the one used in an international research center.
Considering industrial trends and emphasizing professional applicability, CoexecutorRuntime provides a flexible C++/SYCL-based system that provides co-execution support for oneAPI technology. This runtime brings programmers closer to the problem domain, enabling the exploitation of dynamic adaptive strategies that improve efficiency in all types of applications.
https://github.com/thesis-nozal/PhD
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