Improving the effectiveness of software prefetching with adaptive executions

Saavedra, Rafael H.; Park, Daeyeon

doi:10.1109/pact.1996.552556

Cited by 19 publications

(13 citation statements)

References 19 publications

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“…In their work, the compiler inserts load instructions with hints indicating memory access patterns, and the information is consumed by hardware prefetchers. Saavedra and Park [1996] proposed a runtimeadaptive software prefetching mechanism to adjust prefetch distances and degree. Luk and Mowry [1998] proposed a hardware-based filtering mechanism for harmful software prefetch requests.…”

Section: Related Workmentioning

confidence: 99%

When Prefetching Works, When It Doesn’t, and Why

Lee

Kim

Vuduc

2012

ACM Trans. Archit. Code Optim.

113

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In emerging and future high-end processor systems, tolerating increasing cache miss latency and properly managing memory bandwidth will be critical to achieving high performance. Prefetching, in both hardware and software, is among our most important available techniques for doing so; yet, we claim that prefetching is perhaps also the least well-understood.Thus, the goal of this study is to develop a novel, foundational understanding of both the benefits and limitations of hardware and software prefetching. Our study includes: source code-level analysis, to help in understanding the practical strengths and weaknesses of compiler-and software-based prefetching; a study of the synergistic and antagonistic effects between software and hardware prefetching; and an evaluation of hardware prefetching training policies in the presence of software prefetching requests. We use both simulation and measurement on real systems. We find, for instance, that although there are many opportunities for compilers to prefetch much more aggressively than they currently do, there is also a tangible risk of interference with training existing hardware prefetching mechanisms. Taken together, our observations suggest new research directions for cooperative hardware/software prefetching.

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Section: Related Workmentioning

confidence: 99%

When Prefetching Works, When It Doesn’t, and Why

Lee

Kim

Vuduc

2012

ACM Trans. Archit. Code Optim.

113

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“…Like dynamic feedback, Saavedra and Park [14] propose adaptive execution, which dynamically adapts program execution to changes in program and machine conditions. In addition to execution time, they use performance information collected from hardware monitors.…”

Section: Related Workmentioning

confidence: 99%

High-level adaptive program optimization with ADAPT

Voss

Eigemann

2001

Proceedings of the Eighth ACM SIGPLAN Symposium on Principles and Practices of Parallel Programming

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Compile-time optimization is often limited by a lack of target machine and input data set knowledge. Without this information, compilers may be forced to make conservative assumptions to preserve correctness and to avoid performance degradation. In order to cope with this lack of information at compile-time, adaptive and dynamic systems can be used to perform optimization at runtime when complete knowledge of input and machine parameters is available. This paper presents a compiler-supported high-level adaptive optimization system. Users describe, in a domain specific language, optimizations performed by stand-alone optimization tools and backend compiler flags, as well as heuristics for applying these optimizations dynamically at runtime. The ADAPT compiler reads these descriptions and generates application-specific runtime systems to apply the heuristics. To facilitate the usage of existing tools and compilers, overheads are minimized by decoupling optimization from execution. Our system, ADAPT, supports a range of paradigms proposed recently, including dynamic compilation, parameterization and runtime sampling. We demonstrate our system by applying several optimization techniques to a suite of benchmarks on two target machines. ADAPT is shown to consistently outperform statically generated executables, improving performance by as much as 70%.

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“…Adaptive optimizations: Both Saavedra and Park [26] and Diniz and Rinard [7] adapt programs to knowledge discovered while the program is running. Voss and Eigenmann describe ADAPT [32], a system which can dynamically generate and select program variants.…”

Section: Related Workmentioning

confidence: 99%

A Modal Model of Memory

Mitchell

Carter

Ferrante

2001

Computational Science — ICCS 2001

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Abstract. We consider the problem of automatically guiding program transformations for locality, despite incomplete information due to complicated program structures, changing target architectures, and lack of knowledge of the properties of the input data. Our system, the modal model of memory, uses limited static analysis and bounded runtime experimentation to produce performance formulas that can be used to make runtime locality transformation decisions. Static analysis is performed once per program to determine its memory reference properties, using modes, a small set of parameterized, kernel reference patterns. Once per architectural system, our system automatically performs a set of experiments to determine a family of kernel performance formulas. The system can use these kernel formulas to synthesize a performance formula for any program's mode tree. Finally, with program transformations represented as mappings between mode trees, the generated performance formulas can be used to guide transformation decisions.

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Improving the effectiveness of software prefetching with adaptive executions

Cited by 19 publications

References 19 publications

When Prefetching Works, When It Doesn’t, and Why

When Prefetching Works, When It Doesn’t, and Why

High-level adaptive program optimization with ADAPT

A Modal Model of Memory

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