A Tool for Automatically Suggesting Source-Code Optimizations for Complex GPU Kernels
A Tool for Automatically Suggesting Source-Code Optimizations for Complex GPU Kernels
复制标题
自动为复杂 GPU 内核提供源代码优化建议的工具
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Martin Burtscher
中科院分区：
文献类型：
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作者：
Saeed Taheri;Apan Qasem;Martin Burtscher
从掌上电脑到超级计算机，未来的计算系统无疑将比今天的系统更平行和异质，以提供更多的性能和能源效率。因此，GPU越来越多地用于加速通用应用，包括具有数据依赖性，不规则控制流量和内存访问模式的应用程序。但是，日益增长的复杂性，裸露的内存层次结构，不一致，异质性和并行性将使基于加速器的系统逐渐更难编程。在可预见的将来，绝大多数程序员将不再能够从下一代系统中提取额外的性能或能源节省，因此编程太难了。自动绩效分析和优化建议工具有可能避免这种情况。他们体现了专家知识，并在需要时向软件开发人员提供它。在本文中，我们描述和评估了这样的工具。
Future computing systems, from handhelds to supercomputers, will undoubtedly be more parallel and heterogeneous than todays systems to provide more performance and energy efficiency. Thus, GPUs are increasingly being used to accelerate general purpose applications, including applications with data dependent, irregular control flow and memory access patterns. However, the growing complexity, exposed memory hierarchy, incoherence, heterogeneity, and parallelism will make accelerator based systems progressively more difficult to program. In the foreseeable future, the vast majority of programmers will no longer be able to extract additional performance or energy savings from next generation systems be-cause the programming will be too difficult. Automatic performance analysis and optimization recommendation tools have the potential to avert this situation. They embody expert knowledge and make it available to software developers when needed. In this paper, we describe and evaluate such a tool.
MILEPOST GCC：基于机器学习的研究编译器
DOI：
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发表时间：
2008
期刊：
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影响因子：
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作者：
Ayal Zaks
通讯作者：
Ayal Zaks