high performance computing on graphics processing units: hgpu.org

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Deep Tensor Convolution on Multicores

David Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X

November 23, 2016 by hgpu

GA3C: GPU-based A3C for Deep Reinforcement Learning

Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz

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Tags: Computer science, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, TensorFlow

November 23, 2016 by hgpu

High performance pattern matching and data remanence on graphics processing units

X. Bellekens

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Tags: Aho Corasick, Algorithms, amazon, BMH, CUDA, Digital Forensic, forensic, GPU, pattern matching, PFAC, remanence, Sequence matching, String matching, Suffix Tree, Tesla K20, Tesla K20c

November 22, 2016 by xhgpu

Processing OLTP Workloads on Hybrid CPU/GPU Systems

Mudit Bhatnagar

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Tags: Computer science, Databases, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GT 640, nVidia GeForce GTX Titan, OpenCL, Thesis

November 22, 2016 by hgpu

GPU-accelerated Red Blood Cells Simulations with Transport Dissipative Particle Dynamics

Ansel L. Blumers, Yu-Hang Tang, Zhen Li, Xuejin Li, George E. Karniadakis

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Tags: Biological Physics, Computational Physics, CUDA, MPI, nVidia, Package, Physics, Tesla K20

November 22, 2016 by hgpu

Evaluation of an OpenCL-Based FPGA Platform for Particle Filter

Shunsuke Tatsumi, Masanori Hariyama, Norikazu Ikoma

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Tags: Computer science, FPGA, OpenCL, Particle filtering

November 19, 2016 by hgpu

HIPAcc: A Domain-Specific Language and Compiler for Image Processing

Richard Membarth, Oliver Reiche, Frank Hannig, Jurgen Teich, Mario Korner, Wieland Eckert

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Tags: Algorithms, AMD Radeon R9 290X, ARM, ATI, Compilers, Computer science, CUDA, Heterogeneous systems, Image processing, Intel Xeon Phi, nVidia, OpenCL, Package, Tesla K20

November 19, 2016 by hgpu

How to scale distributed deep learning?

Peter H. Jin, Qiaochu Yuan, Forrest Iandola, Kurt Keutzer

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Tags: Computer science, CUDA, Deep learning, GPU cluster, nVidia, Performance, Tesla K20, Tesla K80

November 19, 2016 by hgpu

Performance Analysis of Parallel Sorting Algorithms using GPU Computing

Neetu Faujdar, SP Ghrera

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Performance, Sorting

November 19, 2016 by hgpu

Lattice QCD simulations using the OpenACC platform

Pushan Majumdar

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Tags: CUDA, HEP, High Energy Physics - Lattice, nVidia, OpenACC, Physics, QCD, Tesla X2090

November 19, 2016 by hgpu

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Pedro Bruel, Marcos Amaris, Alfredo Goldman

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Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 980, Package, Performance, Tesla K40

November 16, 2016 by hgpu

Efficient Communications in Training Large Scale Neural Networks

Linnan Wang, Wei Wu, George Bosilca, Richard Vuduc, Zenglin Xu

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Tags: Caffe, Computer science, CUDA, Deep learning, MPI, Neural networks, nVidia, OpenMPI, Performance

November 16, 2016 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Efficient deep learning inference on end devices

Ouroboros: Virtualized Queues for dynamic memory management

Dynamic Memory Management on GPUs with SYCL

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high performance computing on graphics processing units: hgpu.org

Programming

Deep Tensor Convolution on Multicores

GA3C: GPU-based A3C for Deep Reinforcement Learning

High performance pattern matching and data remanence on graphics processing units

Processing OLTP Workloads on Hybrid CPU/GPU Systems

GPU-accelerated Red Blood Cells Simulations with Transport Dissipative Particle Dynamics

Evaluation of an OpenCL-Based FPGA Platform for Particle Filter

HIPAcc: A Domain-Specific Language and Compiler for Image Processing

How to scale distributed deep learning?

Performance Analysis of Parallel Sorting Algorithms using GPU Computing

Lattice QCD simulations using the OpenACC platform

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Efficient Communications in Training Large Scale Neural Networks

Recent source codes

XaaS containers

microSYCL: SYCL micro-benchmarks repository

SYCL Container

CASS: Cuda-Amd aSSembly

Cluser of smartphones for edge computing application using TensorFlow

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

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