An Event-Triggered Programmable Prefetcher for Irregular Workloads

An Event-Triggered Programmable Prefetcher for Irregular Workloads
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针对不规则工作负载的事件触发可编程预取器

DOI:
10.1145/3173162.3173189
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发表时间:
2018
期刊:
--
影响因子:
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通讯作者:
Ainsworth S
Ainsworth S
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作者:
Ainsworth S

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许多现代工作负载都会计算大量数据,通常会进行不规则的内存访问。当前的架构对于这些工作负载表现不佳,因为现有的预取技术无法捕获内存访问模式;因此,这些应用程序最终会受到严重的内存限制。尽管存在多种技术可以使用遍历模式显式配置预取器,从而获得显着的加速,但它们并不能推广到超出其目标数据结构的范围。相反,我们提出了一种事件触发的可编程预取器,它将通用计算单元的灵活性与基于事件的编程模型相结合,并结合编译器技术,自动从带有注释的原始源代码生成事件。这允许做出更复杂的获取决策,而无需在需要中间结果时停止。使用我们的可编程预取系统,结合从应用程序中提取的小型预取内核,我们在各种图形、数据库和 HPC 工作负载的模拟中实现了平均 3.0 倍的加速。
Many modern workloads compute on large amounts of data, often with irregular memory accesses. Current architectures perform poorly for these workloads, as existing prefetching techniques cannot capture the memory access patterns; these applications end up heavily memory-bound as a result. Although a number of techniques exist to explicitly configure a prefetcher with traversal patterns, gaining significant speedups, they do not generalise beyond their target data structures. Instead, we propose an event-triggered programmable prefetcher combining the flexibility of a general-purpose computational unit with an event-based programming model, along with compiler techniques to automatically generate events from the original source code with annotations. This allows more complex fetching decisions to be made, without needing to stall when intermediate results are required. Using our programmable prefetching system, combined with small prefetch kernels extracted from applications, we achieve an average 3.0x speedup in simulation for a variety of graph, database and HPC workloads.
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