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Dynamic Load Balancing Strategies for Graph Applications on GPUs

Ananya Raval, Rupesh Nasre, Vivek Kumar, Vasudevan R, Sathish Vadhiyar, Keshav Pingali

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Tags: Computer science, CUDA, load balancing, nVidia, Tesla K20

November 5, 2017 by hgpu

A Dynamic Hash Table for the GPU

Saman Ashkiani, Martin Farach-Colton, John D. Owens

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Tags: Computer science, CUDA, Graph theory, Hashing, nVidia, Tesla K40

November 5, 2017 by hgpu

ChainerMN: Scalable Distributed Deep Learning Framework

Takuya Akiba, Keisuke Fukuda, Shuji Suzuki

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Source codes

Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package, Python

November 5, 2017 by hgpu

Automatic Scan Parallelization in OpenMP

Maicol Zegarra, Marcio Pereira, Xavier Martorell, Guido Araujo

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Tags: Algorithms, ARM, Computer science, CUDA, nVidia, OpenCL, OpenMP, Sorting, Tesla K40

October 31, 2017 by hgpu

An efficient GPU algorithm for tetrahedron-based Brillouin-zone integration

Daniel Guterding, Harald O. Jeschke

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Tags: Algorithms, Computational Physics, CUDA, nVidia, nVidia GeForce GTX 970, Physics

October 31, 2017 by hgpu

Performance optimizations for scalable CFD applications on hybrid CPU+MIC heterogeneous computing system with millions of cores

Yong-Xian Wang, Li-Lun Zhang, Wei Liu, Xing-Hua Cheng, Yu Zhuang, Anthony T. Chronopoulos

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Tags: cfd, Computer science, CUDA, Fluid dynamics, Heterogeneous systems, MPI, nVidia, OpenMP, Performance, supercomputer, Tesla M2050, Xeon Phi

October 31, 2017 by hgpu

GooFit 2.0

Henry Schreiner, Christoph Hasse, Bradley Hittle, Himadri Pandey, Michael Sokoloff, Karen Tomko

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Tags: Computer science, CUDA, Mathematical Software, MPI, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 1080 Ti, Package, Probability, Tesla K40, Tesla P100

October 29, 2017 by hgpu

Deep Voice 3: 2000-Speaker Neural Text-to-Speech

Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan O. Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, John Miller

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Tags: Computer science, CUDA, Deep learning, nVidia, Speech recognition, Tesla P100

October 24, 2017 by hgpu

A Fast and Generic GPU-Based Parallel Reduction Implementation

Walid Jradi, Hugo do Nascimento, Wellington Martins

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Tags: Computer science, CUDA, nVidia, OpenCL, Performance, Tesla C2075

October 24, 2017 by hgpu

GPU acceleration and performance of the particle-beam-dynamics code Elegant

J.R. King, I.V. Pogorelov, K.M. Amyx, M. Borland, R. Soliday

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

October 24, 2017 by hgpu

Parallel Matching and Clustering Algorithms on GPUs

Md. Naim

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Tags: Algorithms, Clustering, Computer science, CUDA, nVidia, Tesla K40, Thesis

October 21, 2017 by hgpu

Revisiting the Case of ARM SoCs in High-Performance Computing Clusters

Tyler Fox

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Tags: Benchmarking, Computer science, CUDA, Deep learning, Heterogeneous systems, nVidia, nVidia GeForce GTX 960, nVidia Tegra TX1, SoC, Thesis

October 21, 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

Programming

Dynamic Load Balancing Strategies for Graph Applications on GPUs

A Dynamic Hash Table for the GPU

ChainerMN: Scalable Distributed Deep Learning Framework

Automatic Scan Parallelization in OpenMP

An efficient GPU algorithm for tetrahedron-based Brillouin-zone integration

Performance optimizations for scalable CFD applications on hybrid CPU+MIC heterogeneous computing system with millions of cores

GooFit 2.0

Deep Voice 3: 2000-Speaker Neural Text-to-Speech

A Fast and Generic GPU-Based Parallel Reduction Implementation

GPU acceleration and performance of the particle-beam-dynamics code Elegant

Parallel Matching and Clustering Algorithms on GPUs

Revisiting the Case of ARM SoCs in High-Performance Computing Clusters

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

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