high performance computing on graphics processing units: hgpu.org

Sercan O. Arik, Mike Chrzanowski, Adam Coates, Gregory Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi

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

February 28, 2017 by hgpu

Key Reconciliation with Low-Density Parity-Check Codes for Long-Distance Quantum Cryptography

Mario Milicevic, Chen Feng, Lei M. Zhang, P. Glenn Gulak

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Tags: CUDA, nVidia, nVidia GeForce GTX 1080, Parity-check codes, Physics, Quantum Physics, Security

February 28, 2017 by hgpu

liquidSVM: A Fast and Versatile SVM package

Ingo Steinwart, Philipp Thomann

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Tags: Computer science, CUDA, Java, nVidia, Package, Python, Spark

February 26, 2017 by hgpu

Large-Scale Stochastic Learning using GPUs

Thomas Parnell, Celestine Dunner, Kubilay Atasu, Manolis Sifalakis, Haris Pozidis

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Tags: Algorithms, Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan X, nVidia Quadro M 4000

February 26, 2017 by hgpu

MCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel platforms

A. A. Alves Jr, M. D. Sokoloff

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Tags: Computational Physics, CUDA, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Intel Xeon Phi, Monte Carlo simulation, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan Z, nVidia Quadro K 2200, OpenMP, Package, Physics, Tesla K40

February 22, 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

Large-scale image analysis using docker sandboxing

Model-independent partial wave analysis using a massively-parallel fitting framework

Decoupled Block-Wise ILU(k) Preconditioner on GPU

Performance and Portability of Accelerated Lattice Boltzmann Applications with OpenACC

Billion-scale similarity search with GPUs

An Efficient Multiway Mergesort for GPU Architectures

Deep Voice: Real-time Neural Text-to-Speech

Key Reconciliation with Low-Density Parity-Check Codes for Long-Distance Quantum Cryptography

liquidSVM: A Fast and Versatile SVM package

Large-Scale Stochastic Learning using GPUs

MCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel platforms

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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