While GPUs play an increasingly important role in today's high-performance computers, optimizing GPU performance continues to impose large burdens upon programmers. A major challenge in optimizing codes for GPUs stems from the two levels of hardware parallelism, blocks and threads; each of these levels has significantly different characteristics, requiring different optimization strategies. In this paper, we propose a novel compiler optimization algorithm for GPU parallelism. Our approach is based on the polyhedral model, which has enabled significant advances in program analysis and transformation compared to traditional AST-based frameworks. We extend polyhedral schedules to enable two-level parallelization through the idea of superposition, which integrates separate schedules for block-level and thread-level parallelism. Our experimental results demonstrate that our proposed compiler optimization framework can deliver 1.8x and 2.1x geometric mean improvements on NVIDIA Tesla M2050 and K80 GPUs, compared to a state-of-the-art polyhedral parallel code generator (PPCG) for GPGPUs.
虽然GPU在当今的高性能计算机中扮演着越来越重要的角色，但优化GPU性能仍然给程序员带来了巨大的负担。优化gpu代码的主要挑战来自硬件并行性的两个层面，块和线程;每个级别都有显著不同的特征，需要不同的优化策略。本文提出了一种新的GPU并行编译优化算法。我们的方法基于多面体模型，与传统的基于ast的框架相比，它在程序分析和转换方面取得了重大进展。我们扩展了多面体调度，通过叠加的思想来实现两级并行，它集成了块级和线程级并行的单独调度。我们的实验结果表明，与最先进的gpgpu多面体并行代码生成器(PPCG)相比，我们提出的编译器优化框架可以在NVIDIA Tesla M2050和K80 gpu上提供1.8倍和2.1倍的几何平均改进。
{"title":"Optimized two-level parallelization for GPU accelerators using the polyhedral model","authors":"J. Shirako, Akihiro Hayashi, Vivek Sarkar","doi":"10.1145/3033019.3033022","DOIUrl":"https://doi.org/10.1145/3033019.3033022","url":null,"abstract":"While GPUs play an increasingly important role in today's high-performance computers, optimizing GPU performance continues to impose large burdens upon programmers. A major challenge in optimizing codes for GPUs stems from the two levels of hardware parallelism, blocks and threads; each of these levels has significantly different characteristics, requiring different optimization strategies. In this paper, we propose a novel compiler optimization algorithm for GPU parallelism. Our approach is based on the polyhedral model, which has enabled significant advances in program analysis and transformation compared to traditional AST-based frameworks. We extend polyhedral schedules to enable two-level parallelization through the idea of superposition, which integrates separate schedules for block-level and thread-level parallelism. Our experimental results demonstrate that our proposed compiler optimization framework can deliver 1.8x and 2.1x geometric mean improvements on NVIDIA Tesla M2050 and K80 GPUs, compared to a state-of-the-art polyhedral parallel code generator (PPCG) for GPGPUs.","PeriodicalId":146080,"journal":{"name":"Proceedings of the 26th International Conference on Compiler Construction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123489158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We show how partial redundancy elimination (PRE) can be instantiated to perform provably correct fence elimination for multi-threaded programs running on top of the x86, ARM and IBM Power relaxed memory models. We have implemented our algorithm in the backends of the LLVM compiler infrastructure. The optimisation does not induce an observable overhead at compile-time and can result in up-to 10% speedup on some benchmarks.
{"title":"Partially redundant fence elimination for x86, ARM, and power processors","authors":"Robin Morisset, Francesco Zappa Nardelli","doi":"10.1145/3033019.3033021","DOIUrl":"https://doi.org/10.1145/3033019.3033021","url":null,"abstract":"We show how partial redundancy elimination (PRE) can be instantiated to perform provably correct fence elimination for multi-threaded programs running on top of the x86, ARM and IBM Power relaxed memory models. We have implemented our algorithm in the backends of the LLVM compiler infrastructure. The optimisation does not induce an observable overhead at compile-time and can result in up-to 10% speedup on some benchmarks.","PeriodicalId":146080,"journal":{"name":"Proceedings of the 26th International Conference on Compiler Construction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124216986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Static binary analysis is a key tool to assess the security of third-party binaries and legacy programs. Most forms of binary analysis rely on the availability of two key pieces of information: the program's control-flow graph and function boundaries. However, current tools struggle to provide accurate and precise results, in particular when dealing with hand-written assembly functions and non-trivial control-flow transfer instructions, such as tail calls. In addition, most of the existing solutions are ad-hoc, rely on hand-coded heuristics, and are tied to a specific architecture. In this paper we highlight the challenges faced by an architecture agnostic static binary analysis framework to provide accurate information about a program's CFG and function boundaries without employing debugging information or symbols. We propose a set of analyses to address predicate instructions, noreturn functions, tail calls, and context-dependent CFG. rev.ng, our binary analysis framework based on QEMU and LLVM, handles all the 17 architectures supported by QEMU and produces a compilable LLVM IR. We implement our described analyses on top of LLVM IR. In an extensive evaluation, we test our tool on binaries compiled for MIPS, ARM, and x86-64 using GCC and clang and compare them to the industry's state of the art tool, IDA Pro, and two well-known academic tools, BAP/ByteWeight and angr. In all cases, the quality of the CFG and function boundaries produced by rev.ng is comparable to or improves over the alternatives.
{"title":"rev.ng: a unified binary analysis framework to recover CFGs and function boundaries","authors":"A. Federico, Mathias Payer, G. Agosta","doi":"10.1145/3033019.3033028","DOIUrl":"https://doi.org/10.1145/3033019.3033028","url":null,"abstract":"Static binary analysis is a key tool to assess the security of third-party binaries and legacy programs. Most forms of binary analysis rely on the availability of two key pieces of information: the program's control-flow graph and function boundaries. However, current tools struggle to provide accurate and precise results, in particular when dealing with hand-written assembly functions and non-trivial control-flow transfer instructions, such as tail calls. In addition, most of the existing solutions are ad-hoc, rely on hand-coded heuristics, and are tied to a specific architecture. In this paper we highlight the challenges faced by an architecture agnostic static binary analysis framework to provide accurate information about a program's CFG and function boundaries without employing debugging information or symbols. We propose a set of analyses to address predicate instructions, noreturn functions, tail calls, and context-dependent CFG. rev.ng, our binary analysis framework based on QEMU and LLVM, handles all the 17 architectures supported by QEMU and produces a compilable LLVM IR. We implement our described analyses on top of LLVM IR. In an extensive evaluation, we test our tool on binaries compiled for MIPS, ARM, and x86-64 using GCC and clang and compare them to the industry's state of the art tool, IDA Pro, and two well-known academic tools, BAP/ByteWeight and angr. In all cases, the quality of the CFG and function boundaries produced by rev.ng is comparable to or improves over the alternatives.","PeriodicalId":146080,"journal":{"name":"Proceedings of the 26th International Conference on Compiler Construction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129843833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 26th International Conference on Compiler Construction","authors":"","doi":"10.1145/3033019","DOIUrl":"https://doi.org/10.1145/3033019","url":null,"abstract":"","PeriodicalId":146080,"journal":{"name":"Proceedings of the 26th International Conference on Compiler Construction","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131581627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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