Results 211 to 220 of about 21,831,698 (267)
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Parallel Image Processing with the Block Data Parallel Architecture
IBM Journal of Research and Development, 1996Many digital signal and image processing algorithms can be speeded up by executing them in parallel on multiple processors. The speed of parallel execution is limited by the need for communication and synchronization between processors. In this paper, we present a paradigm for parallel processing that we call the block data flow paradigm (BDFP).
W.E. Alexander +2 more
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Function-Parallel Computation in a Data-Parallel Environment
1993 International Conference on Parallel Processing - ICPP'93 Vol2, 1993Asynchromus problems are those which may be decomposed into a set of independenr sub-tasks which are suitable for concurrent execution. Th function paraIIeIism of these problems cannot normally be direcrly expressed using the data-parallel programming model.
Alex L. Cheung, Anthony P. Reeves
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Loop Parallelism Maximization for Multimedia Data Processing in Mobile Vehicular Clouds
IEEE Transactions on Cloud Computing, 2019Mobile vehicular cloud has become popular with the rapid development of cloud computing and mobile computing. Nested loops are usually the most critical part in multimedia and high performance Digital Signal Processing (DSP) systems which are widely used
Meikang Qiu, Wenyun Dai, A. Vasilakos
semanticscholar +1 more source
Data-Parallel Programming on A Reconfigurable Parallel Computer
IETE Technical Review, 1998We present a preprocessor which converts programs written in a data parallel version of C into standard C to run on CDOT CHiPPS, a reconfigurable parallel computer. The main contribution is the development of rewriting methods and an optimizing protocol for inter-processor communication during barrier synchronization.
Ranjan K Sen +3 more
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Mechanisms for Parallel Data Transport
2012Evolving paradigms of parallel transport mechanisms are necessary to satisfy the ever increasing need of high performing communication systems. Parallel transport mechanisms can be described as a technique to send several data simultaneously using several parallel channels.
Okyere Benya J. +4 more
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Exploitation of control parallelism in data parallel algorithms
Proceedings Frontiers '95. The Fifth Symposium on the Frontiers of Massively Parallel Computation, 2002This paper considers the matrix decomposition A=LDL/sup T/, as a vehicle to explore the improvement in performance obtainable through the execution of multiple streams of control on SIMD architectures. Several methods for partitioning the SIMD array are considered.
V. Garg, D.E. Schimmel
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PipeDream: generalized pipeline parallelism for DNN training
Symposium on Operating Systems Principles, 2019DNN training is extremely time-consuming, necessitating efficient multi-accelerator parallelization. Current approaches to parallelizing training primarily use intra-batch parallelization, where a single iteration of training is split over the available ...
D. Narayanan +7 more
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USP: A Unified Sequence Parallelism Approach for Long Context Generative AI
arXiv.orgSequence parallelism (SP), which divides the sequence dimension of input tensors across multiple computational devices, is becoming key to unlocking the long-context capabilities of generative AI models.
Jiarui Fang, Shangchun Zhao
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Proceedings of the International Workshop on OpenCL, 2020
SYCL™ is a heterogeneous programming framework built on top of modern C++. Data Parallel C++, recently introduced as part of Intel's oneAPI project, is an implementation of SYCL. Data Parallel C++ (DPC++) is being developed as an open-source project on top of Clang and LLVM.
Ben Ashbaugh +7 more
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SYCL™ is a heterogeneous programming framework built on top of modern C++. Data Parallel C++, recently introduced as part of Intel's oneAPI project, is an implementation of SYCL. Data Parallel C++ (DPC++) is being developed as an open-source project on top of Clang and LLVM.
Ben Ashbaugh +7 more
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1994
This article presents a data-parallel language, which has been designed around the concepts of relations and reduction operations. Many parallel machines provide hardware support for reduction operations (such as summing all elements of an array), and these operations are widely used in parallel scientific computing.
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This article presents a data-parallel language, which has been designed around the concepts of relations and reduction operations. Many parallel machines provide hardware support for reduction operations (such as summing all elements of an array), and these operations are widely used in parallel scientific computing.
openaire +1 more source