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Task-Parallel Programming with Constrained Parallelism
IEEE Conference on High Performance Extreme Computing, 2022Task graph programming model (TGPM) has become central to a wide range of scientific computing applications because it enables top-down optimization of parallelism that governs the macro-scale performance.
Tsung-Wei Huang, L. Hwang
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Efficiently Supporting Dynamic Task Parallelism on Heterogeneous Cache-Coherent Systems
International Symposium on Computer Architecture, 2020Manycore processors, with tens to hundreds of tiny cores but no hardware-based cache coherence, can offer tremendous peak throughput on highly parallel programs while being complexity and energy efficient.
Moyang Wang, T. Ta, Lin Cheng, C. Batten
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On Scheduling Parallel Tasks at Twilight [PDF]
Theory of Computing Systems, 2000 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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GPU-based Collaborative Filtering Recommendation System using Task parallelism approach
2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2018 2nd International Conference on, 2018Collaborative filtering is one among the top most preferred techniques when implementing recommendation systems. In recent times, more interest has turned towards parallel GPU-based implementation of collaborative filtering algorithms.
N. Sivaramakrishnan, V. Subramaniyaswamy
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Parallelization using task parallel library with task-based programming model
2014 IEEE 5th International Conference on Software Engineering and Service Science, 2014In order to reduce the complexity of traditional multithreaded parallel programming, this paper explores a new task-based parallel programming using the Microsoft .NET Task Parallel Library (TPL). Firstly, this paper proposes a custom data partitioning optimization method to achieve an efficient data parallelism, and applies it to the matrix ...
Nasser Giacaman +4 more
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Integrating task parallelism with actors
ACM SIGPLAN Notices, 2012This paper introduces a unified concurrent programming model combining the previously developed Actor Model (AM) and the task-parallel Async-Finish Model (AFM). With the advent of multi-core computers, there is a renewed interest in programming models that can support a wide range of parallel programming patterns.
Vivek Sarkar, Shams Imam
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The design of a task parallel library
Proceedings of the 24th ACM SIGPLAN conference on Object oriented programming systems languages and applications, 2009The Task Parallel Library (TPL) is a library for .NET that makes it easy to take advantage of potential parallelism in a program. The library relies heavily on generics and delegate expressions to provide custom control structures expressing structured parallelism such as map-reduce in user programs.
Wolfram Schulte +2 more
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A programming model for deterministic task parallelism [PDF]
Proceedings of the 2011 ACM SIGPLAN Workshop on Memory Systems Performance and Correctness, 2011 The currently dominant programming models to write software for multicore processors use threads that run over shared memory. However, as the core count increases, cache coherency protocols get very complex and ineffective, and maintaining a shared memory abstraction becomes expensive and impractical.
Pratikakis, P. +3 more
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ACM Transactions on Parallel Computing
Thanks to the recognition and promotion of chiplet-based High-Performance Computing (HPC) system design technology by semiconductor industry/market leaders, chiplet-based multi-chip systems have gradually become the mainstream. Unfortunately, programming
Qinyun Cai +5 more
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Thanks to the recognition and promotion of chiplet-based High-Performance Computing (HPC) system design technology by semiconductor industry/market leaders, chiplet-based multi-chip systems have gradually become the mainstream. Unfortunately, programming
Qinyun Cai +5 more
semanticscholar +1 more source
A Novel Inference Algorithm for Large Sparse Neural Network using Task Graph Parallelism
IEEE Conference on High Performance Extreme Computing, 2020The ever-increasing size of modern deep neural network (DNN) architectures has put increasing strain on the hardware needed to implement them. Sparsified DNNs can greatly reduce memory costs and increase throughput over standard DNNs, if the loss of ...
Dian-Lun Lin, Tsung-Wei Huang
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