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6th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array Programming (ARRAY)

作者： Jacob, Dejice Singer, Jeremy Univ Glasgow Sch Comp Sci Glasgow Lanark Scotland

ISBN: (纸本)9781450367172

We present ALPyNA, an automatic loop parallelization framework for Python, which analyzes data dependences within nested loops and dynamically generates CUDA kernels for GPU execution. The ALPyNA system applies classical dependence analysis techniques to discover and exploit potential parallelism. The skeletal structure of the dependence graph is determined statically (if possible) or at runtime;this is combined with type and bounds information discovered at runtime, to auto-generate high-performance kernels for offload to GPU. We demonstrate speedups of up to 1000x relative to the native CPython interpreter across four array-intensive numerical Python benchmarks. Performance improvement is related to both iteration domain size and dependence graph complexity. Nevertheless, this approach promises to bring the benefits of manycore parallelism to application developers.

关键词： code generation nested loop parallelization GPU

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Python Programmers Have GPUs too Automatic Python loop parallelization with Staged Dependence Analysis 15

Python Programmers Have GPUs too Automatic Python Loop Paral...

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15th ACM SIGPLAN International Symposium on Dynamic Languages (DLS)

作者： Jacob, Dejice Trinder, Phil Singer, Jeremy Univ Glasgow Sch Comp Sci Glasgow Lanark Scotland

ISBN: (纸本)9781450369961

Python is a popular language for end-user software development in many application domains. End-users want to harness parallel compute resources effectively, by exploiting commodity manycore technology including GPUs. However, existing approaches to parallelism in Python are esoteric, and generally seem too complex for the typical end-user developer. We argue that implicit, or automatic, parallelization is the best way to deliver the benefits of manycore to end-users, since it avoids domain-specific languages, specialist libraries, complex annotations or restrictive language subsets. Auto-parallelization fits the Python philosophy, provides effective performance, and is convenient for non-expert developers. Despite being a dynamic language, we show that Python is a suitable target for auto-parallelization. In an empirical study of 3000+ open-source Python notebooks, we demonstrate that typical loop behaviour 'in the wild' is amenable to auto-parallelization. We show that staging the dependence analysis is an effective way to maximize performance. We apply classical dependence analysis techniques, then leverage the Python runtime's rich introspection capabilities to resolve additional loop bounds and variable types in a just-in-time manner. The parallel loop nest code is then converted to CUDA kernels for GPU execution. We achieve orders of magnitude speedup over baseline interpreted execution and some speedup (up to 50x, although not consistently) over CPU JIT-compiled execution, across 12 loop-intensive standard benchmarks.

关键词： code generation nested loop parallelization GPU

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Pricing Python Parallelism: A Dynamic Language Cost Model for Heterogeneous Platforms 16

Pricing Python Parallelism: A Dynamic Language Cost Model fo...

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16th ACM SIGPLAN International Symposium on Dynamic Languages (DLS)

作者： Jacob, Dejice Trinder, Phil Singer, Jeremy Univ Glasgow Glasgow Lanark Scotland

ISBN: (纸本)9781450381758

Execution times may be reduced by offloading parallel loop nests to a GPU. Auto-parallelizing compilers are common for static languages, often using a cost model to determine when the GPU execution speed will outweigh the offload overheads. Nowadays scientific software is increasingly written in dynamic languages and would benefit from compute accelerators. The ALPyNA framework analyses moderately complex Python loop nests and automatically JIT compiles code for heterogeneous CPU and GPU architectures. We present the first analytical cost model for auto-parallelizing loop nests in a dynamic language on heterogeneous architectures. Predicting execution time in a language like Python is extremely challenging, since aspects like the element types, size of the iteration space, and amenability to parallelization can only be determined at runtime. Hence the cost model must be both staged, to combine compile and run-time information, and lightweight to minimize runtime overhead. GPU execution time prediction must account for factors like data transfer, block-structured execution, and starvation. We show that a comparatively simple, staged analytical model can accurately determine during execution when it is profitable to offload a loop nest. We evaluate our model on three heterogeneous platforms across 360 experiments with 12 loop-intensive Python benchmark programs. The results show small misprediction intervals and a mean slowdown of just 13.6%, relative to the optimal (oracular) offload strategy.

关键词： nested loop parallelization cost model Python GPU

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Index Dependent nested loops parallelization with an Even Distributed Number of Steps

Informatica (Slovenia)

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Informatica (Slovenia) 2021年第4期45卷 493-506页

作者： Pintér, Ádám Szénási, Sándor John von Neumann Faculty of Informatics Óbuda University Hungary Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Hungary Faculty of Economics J. Selye University Slovakia

Parallel processing of algorithms is an effective way to achieve higher performance on multiprocessor systems rather. During parallelization, it is critical to minimize the difference between the processing time for threads. It is necessary to choose a method that can efficiently distribute the workload evenly across the threads. This paper deals with a special kind of nested loops where the internal loop iterator depends on the outer loop iterator. In such cases, the process can be represented as an upper (or lower) triangular matrix. This paper introduces a method for partitioning the outer loop according to the indices in an almost optimal manner, so that the partial loops in each thread will take nearly the same number of steps. In addition, we examine the potential of a perfect partition and try to determine the maximum (but still meaningful) partition size. © 2021 Slovene Society Informatika. All rights reserved.

关键词： Index distribution nested loop parallelization Optimization Parallel algorithm

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