Adaptive Space-Shared Scheduling for Shared-Memory Parallel Programs

Cho, Younghyun; Oh, Surim; Egger, Bernhard

doi:10.1007/978-3-319-61756-5_9

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…This work is enhanced in Reference 29, where an HPC scheduler applies co‐scheduling and uses virtual machine migration of parallel applications at runtime based on their memory bandwidth requirements is proposed. Cho et al 30 introduce a space‐shared scheduling strategy for parallel applications that use the shared‐memory model. It aims to optimize the performance of concurrent execution of parallel applications by properly assigning a disjoint set of cores to each running application.…”

Section: Related Workmentioning

confidence: 99%

Smart resource allocation of concurrent execution of parallel applications

Silva

Nogueira

Lima

et al. 2021

Concurrency and Computation

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Thread‐level parallelism (TLP) has been widely exploited to optimize computational resource usage in high‐performance systems. However, as many applications do not scale as the number of threads increase, resources will be wasted when the application executes with the maximum possible number of threads (i.e., the default execution) rather than fewer threads (thread throttling) that may use the resources more efficiently. Hence, instead of executing only one application with as many threads as possible, one can run more applications simultaneously by applying thread throttling to each one. The primary outcome of this strategy is a significant reduction in the total execution time and energy consumption when the system needs to execute a list of applications. Given that, we propose a smart resource allocation (SRA) for concurrent parallel application execution. It automatically finds the ideal degree of TLP for each application and guides the simultaneous parallel applications execution. When running 25 well‐known benchmarks on three multicore systems and comparing SRA to state‐of‐the‐art strategies (e.g., Batch, Equal policy, and Scalability), SRA improves the EDP by 87.4% over the Batch strategy; 75.5% over the Equal policy; and 38.8% over the scalability strategy.

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Section: Related Workmentioning

confidence: 99%

Smart resource allocation of concurrent execution of parallel applications

Silva

Nogueira

Lima

et al. 2021

Concurrency and Computation

View full text Add to dashboard Cite

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“…Finally, Cho et al [8] present a simple analytical model that determines a proper number of allocated threads for a set of simultaneously running applications, and Cho et al [7] consider the utilization of both CPU cores and memory controllers to determine a proper number of threads for the configuration of co-located applications. Moreover, DoPE [24] and Parcae [25] are systems that perform automatic configuration of the runtime system for various techniques.…”

Section: Related Workmentioning

confidence: 99%

Chunking for Dynamic Linear Pipelines

Mastoras

Groß

2019

ACM Trans. Archit. Code Optim.

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Dynamic scheduling and dynamic creation of the pipeline structure are crucial for efficient execution of pipelined programs. Nevertheless, dynamic systems imply higher overhead than static systems. Therefore, chunking is the key to decrease the synchronization and scheduling overhead by grouping activities. We present a chunking algorithm for dynamic systems that handles dynamic linear pipelines, which allow the number and duration of stages to be determined at run-time. The evaluation on 44 cores shows that chunking brings the overhead of dynamic scheduling down to that of a static scheduler, and it enables efficient and scalable execution of fine-grained dynamic linear pipelines. CCS Concepts: • Software and its engineering → Software performance; Runtime environments;

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“…Space-sharing is often favored compared to time-sharing as it usually leads to better performance. But these solutions require a specific OS scheduler or modifications to the parallel runtimes [12,22].…”

Section: Related Workmentioning

confidence: 99%

TINS: A Task-Based Dynamic Helper Core Strategy for In Situ Analytics

Dirand

Colombet

Raffin

2018

Supercomputing Frontiers

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The in situ paradigm proposes to co-locate simulation and analytics on the same compute node to analyze data while still resident in the compute node memory, hence reducing the need for postprocessing methods. A standard approach that proved efficient for sharing resources on each node consists in running the analytics processes on a set of dedicated cores, called helper cores, to isolate them from the simulation processes. Simulation and analytics thus run concurrently with limited interference. In this paper we show that the performance can be improved through a dynamic helper core strategy. We rely on a work stealing scheduler to implement TINS, a task-based in situ framework with an on-demand analytics isolation. The helper cores are dedicated to analytics only when analytics tasks are available. Otherwise the helper cores join the other cores for processing simulation tasks. TINS relies on the Intel R TBB library. Experiments on up to 14,336 cores run a set of representative analytics parallelized with TBB coupled with the hybrid MPI+TBB ExaStamp molecular dynamics code. TINS shows up to 40% performance improvement over various other approaches including the standard helper core.

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Adaptive Space-Shared Scheduling for Shared-Memory Parallel Programs

Cited by 5 publications

References 20 publications

Smart resource allocation of concurrent execution of parallel applications

Smart resource allocation of concurrent execution of parallel applications

Chunking for Dynamic Linear Pipelines

TINS: A Task-Based Dynamic Helper Core Strategy for In Situ Analytics

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