Helper Thread Prefetching Control Framework on Chip Multi-processor

Zhang, Jianxun; Gu, Zhimin; Huang, Yan; Zheng, Ning; Hu, Xiaohan

doi:10.1007/s10766-013-0299-9

Cited by 1 publication

(1 citation statement)

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“…More recently program slicing has been utilised in a multithreaded context to precompute parts of threads to aid branch prediction and to warm caches in the memory hierarchy [13]. While these techniques are somewhat effective [14], they often suffer due to the overheads required to spin up a thread, along with utilising entire core.…”

Section: B -Synthetic Benchmarkmentioning

confidence: 99%

Using Runahead Execution to Hide Memory Latency in High Level Synthesis

Fleming

Thomas

2017

2017 IEEE 25th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)

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Reads and writes to global data in off-chip RAM can limit the performance achieved with HLS tools, as each access takes multiple cycles and usually blocks progress in the application state machine. This can be combated by using data prefetchers, which hide access time by predicting the next memory access and loading it into a cache before it's required. Unfortunately, current prefetchers are only useful for memory accesses with known regular patterns, such as walking arrays, and are ineffective for those that use irregular patterns over application-specific data structures. In this work we demonstrate prefetchers that are tailor made for applications, even if they have irregular memory accesses. This is achieved through program slicing, a static analysis technique that extracts the memory structure of the input code and automatically constructs an application-specific prefetcher. Both our analysis and tool are fully automated and implemented as a new compiler flag in LegUp, an open source HLS tool. In this work we create a theoretical model showing that speedup must be between 1x and 2x, we also evaluate five benchmarks, achieving an average speedup of 1.38x with an average resource overhead of 1.15x.

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Section: B -Synthetic Benchmarkmentioning

confidence: 99%