Page Placement Strategies for GPUs within Heterogeneous Memory Systems
Page Placement Strategies for GPUs within Heterogeneous Memory Systems
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DOI：
10.1145/2694344.2694381
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发表时间：
2015-03
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通讯作者：
Neha Agarwal;D. Nellans;M. Stephenson;Mike O'Connor;S. Keckler
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作者：
Neha Agarwal;D. Nellans;M. Stephenson;Mike O'Connor;S. Keckler
从智能手机到超级计算机的系统越来越异构，由CPU和GPU组成。为了最大限度地提高成本和能源效率，这些系统将越来越多地使用全局可寻址的异构存储器系统，从而使存储器页面放置的选择对性能至关重要。在这项工作中，我们表明，目前的页面放置策略是不够的，以最大限度地提高GPU的性能，在这些异构的内存系统。我们提出了两个新的页面放置策略，提高GPU的性能：一个应用程序不可知的，一个使用应用程序配置文件信息。我们的应用程序不可知策略，带宽感知（BW-AWARE）放置，通过基于系统中可用的聚合内存带宽平衡内存中的页面放置来最大限度地提高GPU吞吐量。我们基于模拟的结果显示，对于GPU计算工作负载，BW-AWARE布局比现有的Linux INTERLEAVE和PART策略平均高出35%和18%。我们通过开发一种基于编译器的分析机制，为程序员提供有关GPU应用程序数据结构访问模式的信息，从而建立在BW-AWARE放置的基础上。结合这些信息与简单的程序注释提示内存放置，我们的提示为基础的页面放置方法执行90%的甲骨文页面放置的平均水平，在很大程度上减轻了昂贵的动态页面跟踪和迁移的需要。
Systems from smartphones to supercomputers are increasingly heterogeneous, being composed of both CPUs and GPUs. To maximize cost and energy efficiency, these systems will increasingly use globally-addressable heterogeneous memory systems, making choices about memory page placement critical to performance. In this work we show that current page placement policies are not sufficient to maximize GPU performance in these heterogeneous memory systems. We propose two new page placement policies that improve GPU performance: one application agnostic and one using application profile information. Our application agnostic policy, bandwidth-aware (BW-AWARE) placement, maximizes GPU throughput by balancing page placement across the memories based on the aggregate memory bandwidth available in a system. Our simulation-based results show that BW-AWARE placement outperforms the existing Linux INTERLEAVE and LOCAL policies by 35% and 18% on average for GPU compute workloads. We build upon BW-AWARE placement by developing a compiler-based profiling mechanism that provides programmers with information about GPU application data structure access patterns. Combining this information with simple program-annotated hints about memory placement, our hint-based page placement approach performs within 90% of oracular page placement on average, largely mitigating the need for costly dynamic page tracking and migration.