Context-Aware Memory Profiling for Speculative Parallelism

Kim, Changsu; Kim, Ju-Hyun; Kang, Juwon; Lee, Jae Woo; Kim, Hanjun

doi:10.1109/hipc.2017.00045

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…For effective memory management fine-grained data access pattern information is necessary. One straightforward way for data access pattern extraction is tracing each memory access [34,37,45,54]. This approach usually incurs too high overhead to apply online, because it adds the instrumentation overhead to every memory access.…”

Section: Data Access Monitoringmentioning

confidence: 99%

Daos

Park

Bhowmik

Uta³

2022

Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing

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In data-intensive workloads, data placement and memory management are inherently difficult: the programmer and the operating system have to choose between (combinations of) DRAM and storage, replacement policies, as well as paging sizes. Efficient memory management is based on fine-grained data access patterns driving placement decisions. Current solutions in this space cannot be applied to general workloads and production systems due to either unrealistic assumptions or prohibitive monitoring overheads.To overcome these issues, we introduce DAOS, an open-source system for general data access-aware memory management. DAOS provides a data access monitoring framework that utilizes practical best-effort trade-offs between overhead and accuracy. The memory management engine of DAOS allows users to implement their access-aware management with no code, just simple configuration schemes. For system administrators, DAOS provides a runtime system that auto-tunes the schemes for user-defined objectives in a finite time. We evaluated DAOS on commercial service production systems as well as state-of-the-art benchmarks. DAOS achieves up to 12% performance improvement and 91% memory saving. DAOS is upstreamed and available in the Linux kernel.

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Section: Data Access Monitoringmentioning

confidence: 99%

Daos

Park

Bhowmik

Uta³

2022

Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing

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PROMPT: A Fast and Extensible Memory Profiling Framework

Xu,

Chon,

et al. 2024

Proc. ACM Program. Lang.

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Memory profiling captures programs’ dynamic memory behavior, assisting programmers in debugging, tuning, and enabling advanced compiler optimizations like speculation-based automatic parallelization. As each use case demands its unique program trace summary, various memory profiler types have been developed. Yet, designing practical memory profilers often requires extensive compiler expertise, adeptness in program optimization, and significant implementation effort. This often results in a void where aspirations for fast and robust profilers remain unfulfilled. To bridge this gap, this paper presents PROMPT, a framework for streamlined development of fast memory profilers. With PROMPT, developers need only specify profiling events and define the core profiling logic, bypassing the complexities of custom instrumentation and intricate memory profiling components and optimizations. Two state-of-the-art memory profilers were ported with PROMPT where all features preserved. By focusing on the core profiling logic, the code was reduced by more than 65% and the profiling overhead was improved by 5.3× and 7.1× respectively. To further underscore PROMPT’s impact, a tailored memory profiling workflow was constructed for a sophisticated compiler optimization client. In 570 lines of code, this redesigned workflow satisfies the client’s memory profiling needs while achieving more than 90% reduction in profiling overhead and improved robustness compared to the original profilers.

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Context-Aware Memory Profiling for Speculative Parallelism

Cited by 2 publications

References 28 publications

Daos

Daos

PROMPT: A Fast and Extensible Memory Profiling Framework

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