high performance computing on graphics processing units: hgpu.org

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The Feasibility of Using OpenCL Instead of OpenMP for Parallel CPU Programming

Kamran Karimi

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Tags: ATI, Computer science, OpenCL, OpenMP, Performance

March 25, 2015 by hgpu

Data driven scheduling approach for the multi-node multi-GPU Cholesky decomposition

Yuki Tsujita, Toshio Endo

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Tags: Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Task scheduling, Tesla K20

March 25, 2015 by hgpu

Parallelization, Scalability, and Reproducibility in Next-Generation Sequencing Analysis

Johannes Koster

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Tags: Algorithms, Bayesian, Bioinformatics, Biology, Filtering, Next-Generation sequencing, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 780, OpenCL, Package, Thesis

March 23, 2015 by hgpu

Curracurrong: a stream processing system for distributed environments

Vasvi Kakkad

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Tags: Algorithms, Computer science, CUDA, OpenCL, Thesis

March 23, 2015 by hgpu

GPU Kernels for High-Speed 4-Bit Astrophysical Data Processing

Peter Klages, Kevin Bandura, Nolan Denman, Andre Recnik, Jonathan Sievers, Keith Vanderlinde

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Tags: AMD Radeon R9 280X, Astrophysics, ATI, Instrumentation and Methods for Astrophysics, OpenCL, Package

March 23, 2015 by hgpu

Massively Parallel Construction of the Cell Graph

Krzysztof Kaczmarski, Pawel Rzazewski, Albert Wolant

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Tags: Algorithms, Computer science, CUDA, nVidia, Robotics, Tesla K40

March 23, 2015 by hgpu

A Financial Benchmark for GPGPU Compilation

Christian Andreetta, Vivien Begot, Jost Berthold, Martin Elsman, Troels Henriksen, Maj-Britt Nordfang, Cosmin E. Oancea

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Tags: Benchmarking, Computer science, CUDA, Finance, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 780 Ti

March 23, 2015 by hgpu

PTX2Kernel: Converting PTX Code into Compilable Kernels

Qiankun Dong, Tao Li, Shuai Zhang, Xiaofan Jiao, Jiabing Leng

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 480, PTX, Tesla K40

March 22, 2015 by hgpu

Speeding Up Computer Vision Applications on Mobile Computing Platforms

Luna Backes

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Tags: Computer science, Computer vision, nVidia, OpenCL, Thesis

March 22, 2015 by hgpu

Raising the Bar for Using GPUs in Software Packet Processing

Anuj Kalia, Dong Zhou, Michael Kaminsky, David G. Andersen

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 650 Ti, nVidia GeForce GTX 980, Performance, Tesla K20

March 22, 2015 by hgpu

Single stream parallelization of generalized LSTM-like RNNs on a GPU

Kyuyeon Hwang, Wonyong Sung

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Tags: Algorithms, Computer science, CUDA, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, Tesla K40

March 22, 2015 by hgpu

CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

Weifeng Liu, Brian Vinter

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Tags: Algorithms, AMD Radeon R9 290X, Benchmarking, Computer science, CUDA, Intel Xeon Phi, Mathematical Software, nVidia, nVidia GeForce GTX 980, OpenCL, Package, Performance, Sparse matrix

March 20, 2015 by hgpu

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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high performance computing on graphics processing units: hgpu.org

Programming

The Feasibility of Using OpenCL Instead of OpenMP for Parallel CPU Programming

Data driven scheduling approach for the multi-node multi-GPU Cholesky decomposition

Parallelization, Scalability, and Reproducibility in Next-Generation Sequencing Analysis

Curracurrong: a stream processing system for distributed environments

GPU Kernels for High-Speed 4-Bit Astrophysical Data Processing

Massively Parallel Construction of the Cell Graph

A Financial Benchmark for GPGPU Compilation

PTX2Kernel: Converting PTX Code into Compilable Kernels

Speeding Up Computer Vision Applications on Mobile Computing Platforms

Raising the Bar for Using GPUs in Software Packet Processing

Single stream parallelization of generalized LSTM-like RNNs on a GPU

CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

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