Thread reinforcer: Dynamically determining number of threads via OS level monitoring

Pusukuri, Kishore Kumar; Gupta, Rajiv; Bhuyan, Laxmi N.

doi:10.1109/iiswc.2011.6114208

Cited by 90 publications

(51 citation statements)

References 35 publications

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“…This same load imbalance issue was also discovered in the work of Thread Reinforcer [11]. However, our methodology clearly identified a scheduling issue from the very start, whereas Thread Reinforcer determined the best number of cores to run on.…”

Section: Load Imbalance In Swaptionsmentioning

confidence: 78%

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Deconstructing the overhead in parallel applications

Roth

Best²,

Mustard

et al. 2012

2012 IEEE International Symposium on Workload Characterization (IISWC)

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Abstract-Performance problems in parallel programs manifest as lack of scalability. These scalability issues are often very difficult to debug. They can stem from synchronization overhead, poor thread scheduling decisions, or contention for hardware resources, such as shared caches. Traditional profiling tools attribute program cycles to different functions, but do not generate immediate insight into issues limiting scalability. Profiling information is very program-specific and is usually processed manually by a human expert in a time-consuming and cumbersome process.Our experience in tuning performance of parallel applications led us to discover that performance tuning can be considerably simplified, and even to some degree automated, if profiling measurements are organized according to several intuitive performance factors common to most parallel programs. In this work we present these factors and propose a hierarchical framework composing them. We present three case studies where analyzing profiling data according to the proposed principle led us to improve performance of three parallel programs by a factor of 6-20×. Our work lays foundation for new ways of organizing and visualizing profiling data in performance tuning tools.

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Section: Load Imbalance In Swaptionsmentioning

confidence: 78%

“…Examples of the more recent work in this area include Thread Reinforcer [11] and Feedback-Driven Threading [9]. In both cases, the algorithms aim to find the optimal number of threads in a parallel program, and show a good example of the kind of optimization that could be built on top of our framework.…”

Section: Resultsmentioning

confidence: 99%

Deconstructing the overhead in parallel applications

Roth

Best²,

Mustard

et al. 2012

2012 IEEE International Symposium on Workload Characterization (IISWC)

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“…A thread is assigned into each sub-BF list to do matching. An effective way for assigning thread number is equal to that of CPU cores [8]. There are two major objectives of designing Par-BF.…”

Section: Introductionmentioning

confidence: 99%

Par-BF: A Parallel Partitioned Bloom Filter for Dynamic Data Sets

Liu

et al. 2014

2014 International Workshop on Data Intensive Scalable Computing Systems

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Compared with a hash table, a Bloom Filter (BF) is more space-efficient for supporting fast matching though resulting in a controllable and acceptable false positive probability. The space size of the basic BF is predetermined based on the expected number of elements to be stored. However, we cannot predict the scale of a BF space for dynamic sets. The two existing solutions for supporting dynamic sets, Scalable BF (SBF) and Dynamic BF (DBF), still face some challenges on system performance and memory overhead.This paper presents a new BF for dynamic data sets, called Partitioned BF (Par-BF). Compared with DBF and SBF, the size and the range of the false positive probability can be calculated by a group of formulas to leverage a sweet spot between high-performance and low-overhead. Moreover, Par-BF supports parallel fast matching which can improve the overall throughput. From our trace-driven experimental results, the IOPS of Par-BF outperforms that of DBF and SBF from 6X to 10X, and from 2X to 4X, respectively. Meanwhile, through our proposed garbage collection policy, the memory overhead of Par-BF is less than half of the memory usage of SBF.

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“…Other related works use varying techniques and metrics to control single-program parallelism [43][44][45][46][47][48][49][50][51], but similarly do not explore multiprogramming.…”

Section: Shared Memory Systemsmentioning

confidence: 99%

Efficient multiprogramming for multicores with SCAF

Creech

Kotha

Barua

2013

Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture

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As hardware becomes increasingly parallel and the availability of scalable parallel software improves, the problem of managing multiple multithreaded applications (processes) becomes important. Malleable processes, which can vary the number of threads used as they run [1], enable sophisticated and flexible resource management.Although many existing applications parallelized for SMPs with parallel runtimes are in fact already malleable, deployed run-time environments provide no interface nor any strategy for intelligently allocating hardware threads or even preventing oversubscription. Prior research methods either depend upon profiling applications ahead of time in order to make good decisions about allocations, or do not account for process efficiency at all, leading to poor performance. None of these prior methods have been adapted widely in practice. This paper presents the Scheduling and Allocation with Feedback (SCAF) system: a drop-in runtime solution which supports existing malleable applications in making intelligent allocation decisions based on observed efficiency without any changes to semantics, program modification, offline profiling, or even recompilation. Our existing implementation can control most unmodified OpenMP applications. Other malleable threading libraries can also easily be supported with small modifications, without requiring application modification or recompilation.In this work, we present the SCAF daemon and a SCAF-aware port of the GNU OpenMP runtime. We present a new technique for estimating process efficiency purely at runtime using available hardware counters, and demonstrate its effectiveness in aiding allocation decisions.We evaluated SCAF using NAS NPB parallel benchmarks on five commodity parallel platforms, enumerating architectural features and their effects on our scheme.We measured the benefit of SCAF in terms of sum of speedups improvement (a common metric for multiprogrammed environments) when running all benchmark pairs concurrently compared to equipartitioning -the best existing competing scheme in the literature. If the sum of speedups with SCAF is within 5% of equipartitioning (i.e., improvement factor is 0.95X < improvement factor in sum of speedups < 1.05X), then we deem SCAF to break even. Less than 0.95X is considered a slowdown; greater than 1.05X is an improvement. We found that SCAF improves on equipartitioning on 4 out of 5 machines, breaking even or improving in 80-89% of pairs and showing a mean improvement of 1.11-1.22X for benchmark pairs for which it shows an improvement, depending on the machine.Since we are not aware of any widely available tool for equipartitioning, we also compare SCAF against multiprogramming using unmodified OpenMP, which is the only environment available to end-users today. SCAF improves or breaks even on the unmodified OpenMP runtimes for all 5 machines in 72-100% of pairs, with a mean improvement of 1.27-1.7X, depending on the machine.

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Thread reinforcer: Dynamically determining number of threads via OS level monitoring

Cited by 90 publications

References 35 publications

Deconstructing the overhead in parallel applications

Deconstructing the overhead in parallel applications

Par-BF: A Parallel Partitioned Bloom Filter for Dynamic Data Sets

Efficient multiprogramming for multicores with SCAF

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