Scalability analysis of SPMD codes using expectations

Coarfa, Cristian; Mellor-Crummey, John; Froyd, Nathan; Dotsenko, Yuri

doi:10.1145/1274971.1274976

Cited by 32 publications

(18 citation statements)

References 34 publications

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“…In [7], the authors use call path profiles and expectations on the cost differences between executions to estimate the scalability costs incurred by different parts of the program. estima uses both hardware and software cycles to extrapolate the scalability of the application as a whole.…”

Section: Related Workmentioning

confidence: 99%

Estima

Chatzopoulos

Dragojević

Guerraoui

2017

ACM Trans. Parallel Comput.

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This article presents estima, an easy-to-use tool for extrapolating the scalability of in-memory applications. estima is designed to perform a simple yet important task: Given the performance of an application on a small machine with a handful of cores, estima extrapolates its scalability to a larger machine with more cores, while requiring minimum input from the user. The key idea underlying estima is the use of stalled cycles (e.g., cycles that the processor spends waiting for missed cache line fetches or busy locks). estima measures stalled cycles on a few cores and extrapolates them to more cores, estimating the amount of waiting in the system. estima can be effectively used to predict the scalability of in-memory applications for bigger execution machines. For instance, using measurements of memcached and SQLite on a desktop machine, we obtain accurate predictions of their scalability on a server. Our extensive evaluation shows the effectiveness of estima on a large number of in-memory benchmarks. INTRODUCTIONCommodity machines nowadays have hundreds of gigabytes of memory. This enables building performance-critical parallel applications, such as databases and key-value stores, that keep their datasets in main memory. This way, applications avoid slow secondary storage and networks, leaving the CPU as the main performance bottleneck [9,12,26,30]. Understanding the performance of these applications proves to be hard, since the number of CPU cores available during the deployment of a parallel application can be significantly higher than that during its development and testing. Applications developed today can be tested on machines with 16 or 24 cores, but in a few years the same applications are likely to be run on machines with 64 or even more cores.

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Section: Related Workmentioning

confidence: 99%

Estima

Chatzopoulos

Dragojević

Guerraoui

2017

ACM Trans. Parallel Comput.

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show abstract

“…In [7], the authors use call path profiles and expectations on the cost differences between executions to estimate the scalability costs incurred by different parts of the program. ESTIMA uses both hardware and software cycles to extrapolate the scalability of the application itself, identifying bottlenecks in the process.…”

Section: Related Workmentioning

confidence: 99%

Estima

Chatzopoulos

Dragojević

Guerraoui

2016

Proceedings of the 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

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This paper presents ESTIMA, an easy-to-use tool for extrapolating the scalability of in-memory applications. ESTIMA is designed to perform a simple, yet important task: given the performance of an application on a small machine with a handful of cores, ESTIMA extrapolates its scalability to a larger machine with more cores, while requiring minimum input from the user. The key idea underlying ESTIMA is the use of stalled cycles (e.g. cycles that the processor spends waiting for various events, such as cache misses or waiting on a lock). ESTIMA measures stalled cycles on a few cores and extrapolates them to more cores, estimating the amount of waiting in the system. ESTIMA can be effectively used to predict the scalability of in-memory applications. For instance, using measurements of memcached and SQLite on a desktop machine, we obtain accurate predictions of their scalability on a server. Our extensive evaluation on a large number of in-memory benchmarks shows that ESTIMA has generally low prediction errors.

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“…We argue that our approach is much simpler to implement in a tool. Coarfa et al automatically compare pairs of measurements at different scales to identify scalability bottlenecks [10], whereas our approach creates explicit predictive models that describe the scaling behavior beyond the range of measurements. Barnes et al use regression analysis to predict the scalability of applications [2] and is probably the most similar work.…”

Section: Related Workmentioning

confidence: 99%

Using automated performance modeling to find scalability bugs in complex codes

Calotoiu

Hoefler

Poke

et al. 2013

Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

111

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Many parallel applications suffer from latent performance limitations that may prevent them from scaling to larger machine sizes. Often, such scalability bugs manifest themselves only when an attempt to scale the code is actually being made-a point where remediation can be difficult. However, creating analytical performance models that would allow such issues to be pinpointed earlier is so laborious that application developers attempt it at most for a few selected kernels, running the risk of missing harmful bottlenecks. In this paper, we show how both coverage and speed of this scalability analysis can be substantially improved. Generating an empirical performance model automatically for each part of a parallel program, we can easily identify those parts that will reduce performance at larger core counts. Using a climate simulation as an example, we demonstrate that scalability bugs are not confined to those routines usually chosen as kernels.

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Scalability analysis of SPMD codes using expectations

Cited by 32 publications

References 34 publications

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Using automated performance modeling to find scalability bugs in complex codes

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