PufferFish: NUMA-Aware Work-stealing Library using Elastic Tasks

Due to the challenges in providing adequate memory access to many cores on a single processor, Multi-Die and Multi-Socket based multicore systems are becoming mainstream. These systems offer cache-coherent Non-Uniform Memory Access (NUMA) across several memory banks and cache hierarchy to increase m...

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Veröffentlicht in:Proceedings - International Conference on High Performance Computing S. 251 - 260
1. Verfasser: Kumar, Vivek
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2020
Schlagworte:
async-finish Programming Model
Benchmark testing
Computational modeling
Load management
Multicore processing
NUMA
Parallel processing
Parallel programming
Runtime
Work-Stealing
ISSN:2640-0316
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Zusammenfassung:Due to the challenges in providing adequate memory access to many cores on a single processor, Multi-Die and Multi-Socket based multicore systems are becoming mainstream. These systems offer cache-coherent Non-Uniform Memory Access (NUMA) across several memory banks and cache hierarchy to increase memory capacity and bandwidth. Random work-stealing is a widely used technique for dynamic load balancing of tasks on multicore processors. However, it scales poorly on such NUMA systems for memory-bound applications due to cache misses and remote memory access latency. Hierarchical Place Tree (HPT) [1] is a popular approach for improving the locality of a task-based parallel programming model, albeit it requires the programmer to map the dynamically unfolding tasks over a NUMA system evenly. Specifying data-affinity hints provides a more natural way to map the tasks than HPT. Still, a scalable work-stealing implementation for the same is mostly unexplored for modern NUMA systems. This paper presents PufferFish, a new async-finish parallel programming model and work-stealing runtime for NUMA systems that provide a close coupling of the data-affinity hints provided for an asynchronous task with the HPTs in Habanero C/C++ library (HClib). PufferFish introduces Hierarchical Elastic Tasks (HET) that improves the locality by shrinking itself to run on a single worker inside a place or puffing up across multiple workers depending on the work imbalance at a particular place in an HPT. We use a set of widely used memory-bound benchmarks exhibiting regular and irregular execution graphs for evaluating PufferFish. On these benchmarks, we show that PufferFish achieves a geometric mean speedup of 1.5× and 1.9× over HPT implementation in HClib and random work-stealing in CilkPlus, respectively, on a 32-core NUMA AMD EPYC processor.
ISSN:2640-0316
DOI:10.1109/HiPC50609.2020.00039