CaL: Extending Data Locality to Consider Concurrency for Performance Optimization

Liu, Yuhang; Sun, Xian-He

doi:10.1109/tbdata.2017.2753825

Cited by 5 publications

(3 citation statements)

References 54 publications

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“…Gupta et al [23] quantified the data access locality as a conditional probability. Liu and Sun [24] proposed a concurrency-aware data access locality metric, which can accurately reflect the combined impact of data access locality and concurrency in modern memory architectures.…”

Section: Related Workmentioning

confidence: 99%

A Study on Modeling and Optimization of Memory Systems

Liu

Espina

Sun

2021

J. Comput. Sci. Technol.

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Accesses Per Cycle (APC), Concurrent Average Memory Access Time (C-AMAT), and Layered Performance Matching (LPM) are three memory performance models that consider both data locality and memory assess concurrency. The APC model measures the throughput of a memory architecture and therefore reflects the quality of service (QoS) of a memory system. The C-AMAT model provides a recursive expression for the memory access delay and therefore can be used for identifying the potential bottlenecks in a memory hierarchy. The LPM method transforms a global memory system optimization into localized optimizations at each memory layer by matching the data access demands of the applications with the underlying memory system design. These three models have been proposed separately through prior efforts. This paper reexamines the three models under one coherent mathematical framework. More specifically, we present a new memorycentric view of data accesses. We divide the memory cycles at each memory layer into four distinct categories and use them to recursively define the memory access latency and concurrency along the memory hierarchy. This new perspective offers new insights with a clear formulation of the memory performance considering both locality and concurrency. Consequently, the performance model can be easily understood and applied in engineering practices. As such, the memory-centric approach helps establish a unified mathematical foundation for model-driven performance analysis and optimization of contemporary and future memory systems.

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Section: Related Workmentioning

confidence: 99%

A Study on Modeling and Optimization of Memory Systems

Liu

Espina

Sun

2021

J. Comput. Sci. Technol.

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“…(1) input: -the original trace (2) deps -map of sv to its dependence relations (3) output: scheduleSeq -sequence of thread schedule (4) begin (5) for ← 1 to | | − 1 do (6) [ ] ← the thread identifier of the event [ ]; (7) ← the latest event whose thread identifier is [ ] ; (8) ← the location of in the new trace; (9) if [ ].getDep() = null then (10) insert [ ] immediately after ; (11) else (12) ← [ ].getDep(); (13) ← the location of in the new trace; (14) if < then (15) insert [ ] immediately after ; (16) end if (17) end if (18) end for (19) end…”

Section: Experimental Configurationsmentioning

confidence: 99%

“…Prevalent multicore architecture and big data applications today accelerate the development of concurrent systems [16,17]. To fully utilize multicore CPUs, multiple execution flows can run simultaneously, i.e., data access concurrency.…”

Section: Introductionmentioning

confidence: 99%

A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment

Jiang

2018

Security and Communication Networks

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The advent of cloud computation and big data applications has enabled data access concurrency to be prevalent in the distributed cloud environment. In the meantime, security issue becomes a critical problem for researchers to consider. Concurrency bug diagnosis service is to analyze concurrent software and then reason about concurrency bugs in them. However, frequent context switches in concurrent program execution traces will inevitably impact the service performance. To optimize the service performance, this paper presents a static constraint-aware method to simplify concurrent program buggy traces. First, taking the original buggy trace as the operation object, we calculate the maximal sound dependence relations based on the constraint models. Then, we iteratively check the dependent constraints and move forward current event to extend thread execution intervals. Finally, we obtain the simplified trace that is equivalent to the original buggy trace. To evaluate our approach, we conduct a set of experiments on 12 widely used Java projects. Experimental results show that our approach outperforms other state-of-the-art approaches in terms of execution time.

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Data locality optimization based on data migration and hotspots prediction in geo-distributed cloud environment

Zhang

et al. 2019

Knowledge-Based Systems

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CaL: Extending Data Locality to Consider Concurrency for Performance Optimization

Cited by 5 publications

References 54 publications

A Study on Modeling and Optimization of Memory Systems

A Study on Modeling and Optimization of Memory Systems

A Constraint-Aware Optimization Method for Concurrency Bug Diagnosis Service in a Distributed Cloud Environment

Data locality optimization based on data migration and hotspots prediction in geo-distributed cloud environment

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