Results 1 to 10 of about 3,517,478 (209)
torcpy: Supporting task parallelism in Python
SoftwareX, 2020 Task-based parallelism has been established as one of the main forms of code parallelization, where asynchronous tasks are launched and distributed across the processing units of a local machine, a cluster or a supercomputer.
P.E. Hadjidoukas +5 more
doaj +3 more sources
IEEE Access, 2021 Processing-in-memory (PIM) comprises computational logic in the memory domain. It is the most promising solution to alleviate the memory bandwidth problem in deep neural network (DNN) processing.
Young Sik Lee, Tae Hee Han
doaj +2 more sources
Generalized Task Parallelism [PDF]
ACM Transactions on Architecture and Code Optimization, 2015 Existing approaches to automatic parallelization produce good results in specific domains. Yet, it is unclear how to integrate their individual strengths to match the demands and opportunities of complex software. This lack of integration has both practical reasons, as integrating those largely differing approaches into one compiler would impose an ...
Kevin Streit +4 more
semanticscholar +2 more sources
Task parallel assembly language for uncompromising parallelism
Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2021 Achieving parallel performance and scalability involves making compromises between parallel and sequential computation. If not contained, the overheads of parallelism can easily outweigh its benefits, sometimes by orders of magnitude.
M. Rainey +4 more
semanticscholar +2 more sources
Adaptive memory reservation strategy for heavy workloads in the Spark environment [PDF]
PeerJ Computer ScienceThe rise of the Internet of Things (IoT) and Industry 2.0 has spurred a growing need for extensive data computing, and Spark emerged as a promising Big Data platform, attributed to its distributed in-memory computing capabilities.
Bohan Li +6 more
doaj +3 more sources
SWIFT: Using task-based parallelism, fully asynchronous communication, and graph partition-based domain decomposition for strong scaling on more than 100,000 cores [PDF]
Platform for Advanced Scientific Computing Conference, 2016 We present a new open-source cosmological code, called SWIFT, designed to solve the equations of hydrodynamics using a particle-based approach (Smooth Particle Hydrodynamics) on hybrid shared/distributed-memory architectures.
Chalk, Aidan B. G. +3 more
core +5 more sources
Elastic Tasks: Unifying Task Parallelism and SPMD Parallelism with an Adaptive Runtime
European Conference on Parallel Processing, 2015 In this paper, we introduce elastic tasks, a new high-level parallel programming primitive that can be used to unify task parallelism and SPMD parallelism in a common adaptive scheduling framework. Elastic tasks are internally parallel tasks and can run on a single worker or expand to take over multiple workers.
A. Sbîrlea, Kunal Agrawal, Vivek Sarkar
semanticscholar +2 more sources
Itoyori: Reconciling Global Address Space and Global Fork-Join Task Parallelism
International Conference for High Performance Computing, Networking, Storage and Analysis, 2023 This paper introduces Itoyori, a task-parallel runtime system designed to tackle the challenge of scaling task parallelism (more specifically, nested fork-join parallelism) beyond a single node.
Shumpei Shiina, K. Taura
semanticscholar +1 more source
Applied Sciences, 2021 The rapid development of artificial intelligence technology has made deep neural networks (DNNs) widely used in various fields. DNNs have been continuously growing in order to improve the accuracy and quality of the models.
Yingchi Mao +4 more
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Jet: Fast quantum circuit simulations with parallel task-based tensor-network contraction [PDF]
Quantum, 2022 We introduce a new open-source software library $Jet$, which uses task-based parallelism to obtain speed-ups in classical tensor-network simulations of quantum circuits.
Trevor Vincent +6 more
doaj +1 more source