Automatic CPU-GPU communication management and optimization

Jablin, Thomas B.; Prabhu, Prakash; Jablin, James A.; Johnson, Nick; Beard, Stephen R.; August, David I.

doi:10.1145/2345156.1993516

Cited by 33 publications

(48 citation statements)

References 15 publications

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“…It relies on pragmas, but it does not ease the programmer task as much as the Cray, CAPS or PGI compilers. Other experimental compilation tools like CGCM [16] and PAR4ALL [1] aim at automating the process of CPU-GPU communication and the detection of the pieces of code that can run in parallel. The work by Lee and Eigenmann [20] proposes OpenMPC, an API to facilitate translation of OpenMP programs to CUDA, and a compilation system to support it.…”

Section: Related Workmentioning

confidence: 99%

Directive-Based Compilers for GPUs

Ghike

Tejero

Garzarán

et al. 2015

Languages and Compilers for Parallel Computing

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Abstract. General Purpose Graphics Computing Units can be effectively used for enhancing the performance of many contemporary scientific applications. However, programming GPUs using machine-specific notations like CUDA or OpenCL can be complex and time consuming. In addition, the resulting programs are typically fine-tuned for a particular target device. A promising alternative is to program in a conventional and machine-independent notation extended with directives and use compilers to generate GPU code automatically. These compilers enable portability and increase programmer productivity and, if effective, would not impose much penalty on performance. This paper evaluates two such compilers, PGI and Cray. We first identify a collection of standard transformations that these compilers can apply. Then, we propose a sequence of manual transformations that programmers can apply to enable the generation of efficient GPU kernels. Lastly, using the Rodinia Benchmark suite, we compare the performance of the code generated by the PGI and Cray compilers with that of code written in CUDA. Our evaluation shows that the code produced by the PGI and Cray compilers can perform well. For 6 of the 15 benchmarks that we evaluated, the compiler generated code achieved over 85% of the performance of a hand-tuned CUDA version.

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Section: Related Workmentioning

confidence: 99%

Directive-Based Compilers for GPUs

Ghike

Tejero

Garzarán

et al. 2015

Languages and Compilers for Parallel Computing

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show abstract

“…In a recent paper [18], Jablin et al introduce cgcm, a system targeting exactly the same issue. cgcm, just like our scheme is focused on transferring full allocation units.…”

Section: Comparison With Respect To a Fully Dynamic Approachmentioning

confidence: 99%

Static Compilation Analysis for Host-Accelerator Communication Optimization

Amini

Coelho

Irigoin

et al. 2013

Languages and Compilers for Parallel Computing

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Abstract. We present an automatic, static program transformation that schedules and generates ecient memory transfers between a computer host and its hardware accelerator, addressing a well-known performance bottleneck. Our automatic approach uses two simple heuristics: to perform transfers to the accelerator as early as possible and to delay transfers back from the accelerator as late as possible. We implemented this transformation as a middle-end compilation pass in the pips/Par4All compiler. In the generated code, redundant communications due to data reuse between kernel executions are avoided. Instructions that initiate transfers are scheduled eectively at compile-time. We present experimental results obtained with the Polybench 2.0, some Rodinia benchmarks, and with a real numerical simulation. We obtain an average speedup of 4 to 5 when compared to a naïve parallelization using a modern gpu with Par4All, hmpp, and pgi, and 3.5 when compared to an OpenMP version using a 12-core multiprocessor.

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“…Earlier compiler-based approaches [2,8,11,12] insert data transfers statically and use increasingly sophisticated analyses and transformations to avoid redundant transfers. However, the conservativeness inherent in a compiler-only approach cannot avoid all redundant transfers.…”

Section: Coherence Checks Versus Data Transfersmentioning

confidence: 99%

“…The CPU-GPU Communications Manager (CGCM) [8] is a system that automates memory allocations and transfers of data between the CPU and GPU. In that work, CGCM is coupled to an autoparallelization system that produces GPU code from the programs written in C/C++.…”

Section: Cpu-gpu Communications Manager (Cgcm) For X10cudamentioning

confidence: 99%

See 1 more Smart Citation

Fast and efficient automatic memory management for GPUs using compiler-assisted runtime coherence scheme

Pai

Govindarajan

Thazhuthaveetil

2012

Proceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques

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Exploiting the performance potential of GPUs requires managing the data transfers to and from them efficiently which is an errorprone and tedious task. In this paper, we develop a software coherence mechanism to fully automate all data transfers between the CPU and GPU without any assistance from the programmer. Our mechanism uses compiler analysis to identify potential stale accesses and uses a runtime to initiate transfers as necessary. This allows us to avoid redundant transfers that are exhibited by all other existing automatic memory management proposals.We integrate our automatic memory manager into the X10 compiler and runtime, and find that it not only results in smaller and simpler programs, but also eliminates redundant memory transfers. Tested on eight programs ported from the Rodinia benchmark suite it achieves (i) a 1.06x speedup over hand-tuned manual memory management, and (ii) a 1.29x speedup over another recently proposed compiler-runtime automatic memory management system. Compared to other existing runtime-only and compiler-only proposals, it also transfers 2.2x to 13.3x less data on average.

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Automatic CPU-GPU communication management and optimization

Cited by 33 publications

References 15 publications

Directive-Based Compilers for GPUs

Directive-Based Compilers for GPUs

Static Compilation Analysis for Host-Accelerator Communication Optimization

Fast and efficient automatic memory management for GPUs using compiler-assisted runtime coherence scheme

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