Results 1 to 10 of about 3,227,937 (329)

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, A. Bartezzaghi, F. Scheidegger, R. Istrate, C. Bekas, A.C.I. Malossi   +5 more
doaj   +3 more sources

Task Parallelism-Aware Deep Neural Network Scheduling on Multiple Hybrid Memory Cube-Based Processing-in-Memory

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

Automatic Inference of Task Parallelism in Task-Graph-Based Actor Models

IEEE Access, 2018
Automatic inference of task level parallelism is fundamental for ensuring many kinds of safety and liveness properties of parallel applications. For example, two tasks running in parallel may be involved in data races when they have conflicting memory ...
Abu Naser Masud, Bjorn Lisper, Federico Ciccozzi   +2 more
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, Johannes Doerfert, Clemens Hammacher, Andreas Zeller, Sebastian Hack   +4 more
openaire   +2 more sources

Adaptive memory reservation strategy for heavy workloads in the Spark environment [PDF]

PeerJ Computer Science
The 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, Xin He, Junyang Yu, Guanghui Wang, Yixin Song, Shunjie Pan, Hangyu Gu   +6 more
doaj   +3 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. Today, we expect programmers to implement this compromise by optimizing their code manually.
Mike Rainey, Ryan R. Newton, Kyle Hale, Nikos Hardavellas, Simone Campanoni, Peter Dinda, Umut A. Acar   +6 more
openaire   +2 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., Draper, Peter W., Gonnet, Pedro, Schaller, Matthieu   +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.
Alina Sbîrlea, Kunal Agrawal, Vivek Sarkar   +2 more
openaire   +2 more sources

Secure Deep Neural Network Models Publishing Against Membership Inference Attacks Via Training Task Parallelism

IEEE Transactions on Parallel and Distributed Systems, 2021
Vast data and computing resources are commonly needed to train deep neural networks, causing an unaffordable price for individual users. Motivated by the increasing demands of deep learning applications, sharing well-trained models becomes popular.
Yunlong Mao, Wenbo Hong, Boyu Zhu, Zhifei Zhu, Yuan Zhang, Sheng Zhong   +5 more
semanticscholar   +1 more source

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, Lee J. O'Riordan, Mikhail Andrenkov, Jack Brown, Nathan Killoran, Haoyu Qi, Ish Dhand   +6 more
doaj   +1 more source