Results 91 to 100 of about 27,095 (213)
Mainstream parallel array programming on cell [PDF]
, 2011 We present the E] compiler and runtime library for the ‘F’ subset of the Fortran 95 programming language. ‘F’ provides first-class support for arrays, allowing E] to implicitly evaluate array expressions in parallel using the SPU coprocessors of the ...
Keir, P., Cockshott, W.P., Richards, A.
core
Exploiting Data Parallelism in the Image Content Engine [PDF]
, 2006 The Image Content Engine (ICE) is a framework of software and underlying mathematical and physical models that enable scientists and analysts to extract features from Terabytes of imagery and search the extracted features for content relevant to their ...
Miller, W M +3 more
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Engineering ProceedingsThis research paper analyzes and highlights the benefits of parallel processing to enhance performance and computational efficiency in modern computing systems.
Mirela Sino, Ervin Domazet
doaj +1 more source
, 2020 Deep Neural Network (DNN) models have continuously been growing in size in order to improve the accuracy and quality of the models. Moreover, for training of large DNN models, the use of heterogeneous GPUs is inevitable due to the short release cycle of ...
Yi, Chang M +7 more
core +1 more source
Integrating Task and Data Parallelism Using Parallel I/O Techniques
, 1994 Task and data parallelism are traditionally seen as disjoint modes of parallelism. But a lot of multidisciplinary applications can benefit from both types of parallelism.
Rakesh Krishnaiyer +3 more
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Irregular Coarse-Grain Data Parallelism Under LPARX
LPARX is a software development tool for implementing dynamic, irregular scientific applications, such as multilevel multilevel finite difference methods and particle methods, on high performance MIMD parallel architectures. It supports coarse grain data
Scott B. Baden, Scott Kohn
core
Embedding Data Parallelism in Sequential Object Oriented Languages
, 1993 International audience[Excerpt from the introduction] The spreading of Distributed Memory Parallel Computers (DMPCs) is hampered by the fact that writing or porting application programs to such architectures is a difficult, time-consuming and error-prone
Guidec, Frédéric +1 more
core +1 more source
A Multi-Threaded Implementation of Nested Data-Parallelism
, 1997 In previous work, we have proposed a multithreaded execution model for describing nested data-parallelism on distributed multiprocessors in a fashion generic upon the partitioning of data aggregates within the system.
Andrew Wendelborn, Dean Engelhardt
core
Cyclic Data Parallelism for Efficient Parallelism of Deep Neural Networks
Training large deep learning models requires parallelization techniques to scale. In existing methods such as Data Parallelism or ZeRO-DP, micro-batches of data are processed in parallel, which creates two drawbacks: the total memory required to store ...
Oyallon, Edouard, Fournier, Louis
core +1 more source
A parallel data management layer for data mining
, 2006 We propose the design of a data management abstraction level to implement a full set of parallel KDD applications with minimal performance overhead and greater scalability than conventional DBMS, providing a high-level parallel API to be exploited by parallel and out-of-core data mining algorithms.
Coppola M +4 more
openaire +3 more sources