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BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Matthieu Courbariaux, Yoshua Bengio

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, Neural networks, nVidia, nVidia GeForce GTX 750, Package, Python

February 10, 2016 by hgpu

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Nachiket Kapre, Deheng Ye

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Tags: Benchmarking, Code generation, Computer science, CUDA, FPGA, nVidia, Package, Performance, Tesla K20

February 10, 2016 by hgpu

FARGO3D: A new GPU-oriented MHD code

Pablo Benitez-Llambay, Frederic Masset

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Tags: Astrophysics, CUDA, Finite difference, Instrumentation and Methods for Astrophysics, Magnetohydrodynamics, MPI, N-body simulation, nVidia, Package, Physics, Tesla K20

February 10, 2016 by hgpu

Guided Profiling for Auto-Tuning Array Layouts on GPUs

Nicolas Weber, Sandra C. Amend and Michael Goesele

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Tags: Array Layouts, Auto-Tuning, CUDA, Profiling

February 9, 2016 by mergian

High performance high-order numerical methods: applications in ocean modeling

Rajesh Gandham

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Tags: ATI, ATI Radeon HD 7970, cfd, Computer science, CUDA, Fluid dynamics, Numerical simulation, nVidia, nVidia GeForce GTX Titan, OCCA, OpenCL, OpenMP, real time systems, Tesla C2050, Tesla K20, Tesla K40, Thesis

February 8, 2016 by hgpu

Integrating GPGPU computations with CPU coroutines in C++

Pavel A. Lebedev

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Tags: AMD Radeon R7 265, ATI, ATI Radeon HD 7570, CUDA, nVidia, nVidia GeForce GTX 460, nVidia GeForce GTX 580, OpenCL, Physics

February 8, 2016 by hgpu

Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation

Gang Mei, Hong Tian

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GT 640, Package

February 6, 2016 by hgpu

EIE: Efficient Inference Engine on Compressed Deep Neural Network

Song Han, Xingyu Liu, Huizi Mao, Jing Pu, Ardavan Pedram, Mark A. Horowitz, William J. Dally

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia Jetson TK1, Sparse matrix

February 6, 2016 by hgpu

Optimization and Large Scale Computation of an Entropy-Based Moment Closure

C. Kristopher Garrett, Cory Hauck, Judith Hill

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Tags: Algorithms, CUDA, Heterogeneous systems, nVidia, OpenMP, Physics, Tesla K20

February 3, 2016 by hgpu

Deep Learning For Smile Recognition

Patrick O. Glauner

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Tags: Computer science, Computer vision, CUDA, Deep learning, Machine learning, Neural networks, nVidia, Tesla K40

February 3, 2016 by hgpu

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

Ashwin Trikuta Srinath

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Tags: Algorithms, Computer science, CUDA, Finite difference, Fluid dynamics, GPU cluster, Numerical simulation, nVidia, OpenMPI, Tesla K20, Tesla K40, Thesis

February 3, 2016 by hgpu

Algorithms and Heuristics for Scalable Betweenness Centrality Computation on Multi-GPU Systems

Flavio Vella, Giancarlo Carbone, Massimo Bernaschi

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Tags: Algorithms, Computer science, CUDA, Data Structures and Algorithms, Graph theory, nVidia, OpenMPI, Tesla K20, Tesla K80

February 3, 2016 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

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

FARGO3D: A new GPU-oriented MHD code

Guided Profiling for Auto-Tuning Array Layouts on GPUs

High performance high-order numerical methods: applications in ocean modeling

Integrating GPGPU computations with CPU coroutines in C++

Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation

EIE: Efficient Inference Engine on Compressed Deep Neural Network

Optimization and Large Scale Computation of an Entropy-Based Moment Closure

Deep Learning For Smile Recognition

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

Algorithms and Heuristics for Scalable Betweenness Centrality Computation on Multi-GPU Systems

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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