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MPI-GPU parallelism in iterative eigensolvers for block-tridiagonal matrices

A. Lamas Davina, J. E. Roman

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Tags: Algorithms, Computer science, CUDA, Factorization, MPI, nVidia, Tesla K80

February 2, 2017 by hgpu

Autotuning GPU Kernels via Static and Predictive Analysis

Robert V. Lim, Boyana Norris, Allen D. Malony

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Tags: Code generation, Computer science, CUDA, nVidia, Performance, PTX, Tesla K20, Tesla M2050, Tesla M40

February 2, 2017 by hgpu

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

Joao Paulo Tarasconi Ruschel

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Matrix decomposition, nVidia, OpenCL, OpenMP, Package, Performance, Tesla K80, Thesis

January 26, 2017 by hgpu

Accelerating Workloads on FPGAs via OpenCL: A Case Study with OpenDwarfs

Anshuman Verma, Ahmed E. Helal, Konstantinos Krommydas, Wu-Chun Feng

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Tags: Computer science, FPGA, Intel Xeon Phi, N-body simulation, nVidia, OpenCL, performance portability, Tesla C2070, Tesla K20

January 26, 2017 by hgpu

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

Rui Fan, Ke Xu, Jichang Zhao

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Package

January 26, 2017 by hgpu

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean

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Tags: Algorithms, Computer science, CUDA, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, TensorFlow, Tesla K40

January 26, 2017 by hgpu

Astrophysical-oriented Computational multi-Architectural Framework

Dzmitry Razmyslovich

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Tags: Astrophysics, Benchmarking, Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GTX 285, OpenCL, Thesis

January 23, 2017 by hgpu

DeepBach: a Steerable Model for Bach chorales generation

Gaetan Hadjeres, Francois Pachet

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Tags: Artificial intelligence, Computer science, CUDA, Deep learning, Keras, nVidia, nVidia GeForce GTX 980 Ti, Package

January 23, 2017 by hgpu

GPGPU Performance Estimation with Core and Memory Frequency Scaling

Qiang Wang, Xiaowen Chu

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Tags: Computer science, CUDA, Hardware, nVidia, nVidia GeForce GTX 980, Performance

January 23, 2017 by hgpu

A task-driven implementation of a simple numerical solver for hyperbolic conservation laws

Mohamed Essadki, Jonathan Jung, Adam Larat, Milan Pelletier, Vincent Perrier

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Tags: Computer science, CUDA, Mathematical Software, Numerical Analysis, nVidia, Task scheduling, Tesla K40

January 23, 2017 by hgpu

OpenNMT: Open-Source Toolkit for Neural Machine Translation

Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, Alexander M. Rush

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Tags: Computer science, CUDA, Deep learning, Machine learning, NLP, nVidia, nVidia GeForce GTX 1080, Package, Python, Torch

January 19, 2017 by hgpu

Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

Yasuaki Mitani, Fumihiko Ino, Kenichi Hagihara

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Package, String matching

January 19, 2017 by hgpu

CuPBoP-AMD: Extending CUDA to AMD Platforms

CuPBoP: Making CUDA a Portable Language

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

See all packages

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high performance computing on graphics processing units: hgpu.org

MPI-GPU parallelism in iterative eigensolvers for block-tridiagonal matrices

Autotuning GPU Kernels via Static and Predictive Analysis

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

Accelerating Workloads on FPGAs via OpenCL: A Case Study with OpenDwarfs

A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Astrophysical-oriented Computational multi-Architectural Framework

DeepBach: a Steerable Model for Bach chorales generation

GPGPU Performance Estimation with Core and Memory Frequency Scaling

A task-driven implementation of a simple numerical solver for hyperbolic conservation laws

OpenNMT: Open-Source Toolkit for Neural Machine Translation

Parallelizing Exact and Approximate String Matching via Inclusive Scan on a GPU

Recent source codes

CuPBoP-AMD: Extending CUDA to AMD Platforms

Adopter: Automated Deep Learning Optimization via DSL-based Source Code Transformation

ROCm's implementation of Gromacs

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

Most viewed papers (last 30 days)