AMOEBA: a coarse grained reconfigurable architecture for dynamic GPU scaling
AMOEBA: a coarse grained reconfigurable architecture for dynamic GPU scaling
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AMOEBA：用于动态 GPU 扩展的粗粒度可重构架构
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Gayatri Mehta
中科院分区：
文献类型：
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作者：
Xianwei Cheng;Hui Zhao;M. Kandemir;Beilei Jiang;Gayatri Mehta
不同的GPU应用程序表现出不同的可扩展性模式，包括片上网络（NoC）、合并、内存和控制发散以及L1缓存行为。GPU由多个流式多处理器（SM）组成，它们共同决定如何分区和访问共享资源。近年来，SM的扩展出现了不同的路径，朝着规模扩大（更少，更大的SM）和向外扩展（更多，更小的SM）。然而，无论是向上扩展还是向外扩展都不能满足在给定GPU系统上运行的所有应用程序的可扩展性要求，这不可避免地导致某些应用程序的性能下降和资源利用不足。在这项工作中，我们调查的主要设计参数，影响GPU的缩放。然后，我们提出了AMOEBA，通过可重构的SM内核的GPU扩展的解决方案。AMOEBA在运行时监控和预测应用程序的可伸缩性，并调整SM配置以满足程序需求。AMOEBA还支持通过独立融合或拆分动态创建异构SM。AMOEBA是一个基于微架构的解决方案，不需要额外的编程工作或自定义编译器支持。我们的实验评估与应用程序从各种基准套件表明，AMOEBA能够实现最大的性能增益4.3倍，并产生47%的平均性能提高时，考虑到所有的基准测试。
Different GPU applications exhibit varying scalability patterns with network-on-chip (NoC), coalescing, memory and control divergence, and L1 cache behavior. A GPU consists of several Streaming Multi-processors (SMs) that collectively determine how shared resources are partitioned and accessed. Recent years have seen divergent paths in SM scaling towards scale-up (fewer, larger SMs) vs. scale-out (more, smaller SMs). However, neither scaling up nor scaling out can meet the scalability requirement of all applications running on a given GPU system, which inevitably results in performance degradation and resource under-utilization for some applications. In this work, we investigate major design parameters that influence GPU scaling. We then propose AMOEBA, a solution to GPU scaling through reconfigurable SM cores. AMOEBA monitors and predicts application scalability at run-time and adjusts the SM configuration to meet program requirements. AMOEBA also enables dynamic creation of heterogeneous SMs through independent fusing or splitting. AMOEBA is a microarchitecture-based solution and requires no additional programming effort or custom compiler support. Our experimental evaluations with application programs from various benchmark suites indicate that AMOEBA is able to achieve a maximum performance gain of 4.3x, and generates an average performance improvement of 47% when considering all benchmarks tested.
DOI：
10.1109/hpca.2018.00030
发表时间：
2018-02
期刊：
2018 IEEE International Symposium on High Performance Computer Architecture (HPCA)
影响因子：
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作者：
Haonan Wang;Fan Luo;M. Ibrahim;Onur Kayiran;Adwait Jog
通讯作者：
Haonan Wang;Fan Luo;M. Ibrahim;Onur Kayiran;Adwait Jog