high performance computing on graphics processing units: hgpu.org

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GPU-accelerated algorithms for many-particle continuous-time quantum walks

Enrico Piccinini, Claudia Benedetti, Ilaria Siloi, Matteo G. A. Paris, Paolo Bordone

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Tags: Algorithms, Benchmarking, Computational Physics, CUDA, nVidia, nVidia GeForce GTX 980, OpenMP, Physics, Quantum computing, Quantum Physics, Quantum walks, Tesla K40, Tesla K80, Tesla M2050

December 6, 2016 by hgpu

Hadoopcl2: Motivating the design of a distributed, heterogeneous programming system with machine-learning applications

Max Grossman, Mauricio Breternitz, Vivek Sarkar

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Tags: Algorithms, Computer science, Hadoop, Heterogeneous systems, Machine learning, MapReduce, nVidia, OpenCL, Spark, Tesla M2050

November 13, 2016 by hgpu

Sparse-Matrix support for the SkePU library for portable CPU/GPU programming

Vishist Sharma

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenMP, Sparse matrix, Tesla M2050, Thesis

October 22, 2016 by hgpu

Matrix Computations and Optimization in Apache Spark

Reza Bosagh Zadeh, Xiangrui Meng, Alexander Ulanov, Burak Yavuz, Li Pu, Shivaram Venkataraman, Evan Sparks, Aaron Staple, Matei Zaharia

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Machine learning, nVidia, Package, Scala, Spark, Tesla M2050

September 3, 2016 by hgpu

Algorithmic and Software System Support to Accelerate Data Processing in CPU-GPU Hybrid Computing Environments

Kaibo Wang

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Tags: Algorithms, Computer science, CUDA, Databases, Hybrid computing, nVidia, nVidia GeForce GTX 580, Package, Tesla M2050, Thesis

April 12, 2016 by hgpu

Automatic and Explicit Parallelization Approaches for Mathematical Simulation Models

Mahder Gebremedhin

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Tags: Compilers, Computer science, nVidia, OpenCL, Tesla M2050, Thesis

March 12, 2016 by hgpu

Automatic Data Layout Generation and Kernel Mapping for CPU+GPU Architectures

Deepak Majeti, Kuldeep S. Meel, Rajkishore Barik, Vivek Sarkar

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Tags: Algorithms, Compilers, Computer science, Heterogeneous systems, nVidia, OpenCL, Programming techniques, Tesla M2050

March 10, 2016 by hgpu

Portable Programming Models for Heterogeneous Platforms

Deepak Majeti

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Tags: ATI, ATI Radeon HD 5970, ATI Radeon HD 7660, Computer science, Heterogeneous systems, nVidia, OpenCL, Package, Tesla M2050, Thesis

February 8, 2016 by hgpu

CUDA Parallel Algorithms for Forward and Inverse Structural Gravity Problems

Alexander Tsidaev

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Tags: CUDA, Earth and Space Sciences, Geoscience, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 780 Ti, Tesla M2050

January 4, 2016 by hgpu

Exploiting Task-Parallelism on GPU Clusters via OmpSs and rCUDA Virtualization

Adrian Castello, Rafael Mayo, Judit Planas, Enrique S. Quintana-Orti

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Tags: Computer science, CUDA, GPU cluster, nVidia, OmpSs, Tesla C2050, Tesla M2050, Virtualization

October 8, 2015 by hgpu

linalg: Matrix Computations in Apache Spark

Reza Bosagh Zadeh, Xiangrui Meng, Burak Yavuz, Aaron Staple, Li Pu, Shivaram Venkataraman, Evan Sparks, Alexander Ulanov, Matei Zaharia

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Tags: Algorithms, Benchmarking, Computer science, CUBLAS, CUDA, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Scala, Tesla M2050

September 15, 2015 by hgpu

Parallelizing a high-order WENO scheme for complicated flow structures on GPU and MIC

Liang Deng, Fang Wang, Han-Li Bai, Qing-Xin Xu

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Tags: CUDA, Fluid dynamics, Intel Xeon Phi, nVidia, Tesla K20, Tesla M2050

August 18, 2015 by hgpu

CLUEstering: a high-performance density-based clustering library for scientific computing

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high performance computing on graphics processing units: hgpu.org

GPU-accelerated algorithms for many-particle continuous-time quantum walks

Hadoopcl2: Motivating the design of a distributed, heterogeneous programming system with machine-learning applications

Sparse-Matrix support for the SkePU library for portable CPU/GPU programming

Matrix Computations and Optimization in Apache Spark

Algorithmic and Software System Support to Accelerate Data Processing in CPU-GPU Hybrid Computing Environments

Automatic and Explicit Parallelization Approaches for Mathematical Simulation Models

Automatic Data Layout Generation and Kernel Mapping for CPU+GPU Architectures

Portable Programming Models for Heterogeneous Platforms

CUDA Parallel Algorithms for Forward and Inverse Structural Gravity Problems

Exploiting Task-Parallelism on GPU Clusters via OmpSs and rCUDA Virtualization

linalg: Matrix Computations in Apache Spark

Parallelizing a high-order WENO scheme for complicated flow structures on GPU and MIC

Recent source codes

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

HiRep

GPPerf: Energy-Aware GPU Performance Prediction and Optimization Framework

gpuPairHMM: Ultra-fast GPU-based PairHMM for DNA Variant Calling

Gaia AVU-GSR

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

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