high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla C2050

Domain-Specific Languages for Heterogeneous Parallel Computing

HyoukJoong Lee, Kevin J. Brown, Arvind K. Sujeeth, Hassan Chafi, Tiark Rompf, Martin Odersky, Kunle Olukotun

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, Tesla C2050

October 15, 2013 by hgpu

Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations

Jonathan Passerat-Palmbach

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Java, Monte Carlo simulation, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 590, OpenCL, Pseudo-random number generators, Software Engineering, Stochastic simulation, Tesla C2050, Tesla C2075, Tesla K20, Thesis

October 13, 2013 by hgpu

High performance sequence mining using pairwise statistical significance

Yuhong Zhang, Feng Chen

View

Download (PDF)

Source codes

Tags: Algorithms, Bioinformatics, Biology, CUDA, nVidia, Package, Tesla C2050

October 12, 2013 by hgpu

Scalable Fast Multipole Methods on Heterogeneous Architecture

Q. Hu

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Fast multipole method, Heterogeneous systems, N-body simulation, nVidia, nVidia GeForce 8800 GTX, Tesla C1060, Tesla C2050, Thesis

October 9, 2013 by hgpu

Compiler Optimizations for SIMD/GPU/Multicore Architectures

Jun Liu

View

Download (PDF)

Tags: Code generation, Compilers, Computer science, CUDA, Data parallelism, nVidia, Tesla C2050, Thesis

October 3, 2013 by hgpu

Parallel Hyperspectral Unmixing on GPUs

Jose M. P. Nascimento, Jose M. Bioucas-Dias, Jose M. Rodriguez Alves, Vitor Silva, Antonio Plaza

View

Download (PDF)

Tags: Algorithms, Image processing, nVidia, nVidia GeForce GTX 590, nVidia GeForce GTX 680, OpenCL, Tesla C2050

October 2, 2013 by hgpu

Data-parallel Acceleration of PARSEC Black-Scholes Benchmark

August Andren, Patrik Hagernas

View

Download (PDF)

Tags: Benchmarking, Computer science, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 680, OpenACC, Tesla C2050, Tesla M2090

September 30, 2013 by hgpu

Efficient Computation of k-Nearest Neighbour Graphs for Large High-Dimensional Data Sets on GPU Clusters

Ali Dashti, Ivan Komarov, Roshan M. D'Souza

View

Download (PDF)

Tags: Computer science, CUDA, GPU cluster, Nearest neighbour, nVidia, Tesla C2050

September 28, 2013 by hgpu

Trellis: Portability Across Architectures with a High-level Framework

Lukasz G. Szafaryn, Todd Gamblin, Bronis R. de Supinski, Kevin Skadron

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, Tesla C2050

September 27, 2013 by hgpu

Fast k-NNG construction with GPU-based quick multi-select

Ivan Komarov, Ali Dashti, Roshan D'Souza

View

Download (PDF)

Tags: Algorithms, Bioinformatics, Clustering, Computer science, CUDA, Machine learning, Nearest neighbour, nVidia, Tesla C2050

September 25, 2013 by hgpu

BenchFriend: Correlating the Performance of GPU Benchmarks

Shuai Che, Kevin Skadron

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, GPGPU-sim, nVidia, Tesla C2050, Tesla K20

September 23, 2013 by hgpu

Processing MPI Derived Datatypes on Noncontiguous GPU-Resident Data

John Jenkins, James Dinan, Pavan Balaji, Tom Peterka, Nagiza F. Samatova, Rajeev Thakur

View

Download (PDF)

Tags: Algorithms, Benchmarking, Computer science, CUDA, MPI, nVidia, Tesla C2050

September 23, 2013 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

* * *

high performance computing on graphics processing units: hgpu.org

Domain-Specific Languages for Heterogeneous Parallel Computing

Contributions to parallel stochastic simulation: Application of good software engineering practices to the distribution of pseudorandom streams in hybrid Monte-Carlo simulations

High performance sequence mining using pairwise statistical significance

Scalable Fast Multipole Methods on Heterogeneous Architecture

Compiler Optimizations for SIMD/GPU/Multicore Architectures

Parallel Hyperspectral Unmixing on GPUs

Data-parallel Acceleration of PARSEC Black-Scholes Benchmark

Efficient Computation of k-Nearest Neighbour Graphs for Large High-Dimensional Data Sets on GPU Clusters

Trellis: Portability Across Architectures with a High-level Framework

Fast k-NNG construction with GPU-based quick multi-select

BenchFriend: Correlating the Performance of GPU Benchmarks

Processing MPI Derived Datatypes on Noncontiguous GPU-Resident Data

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)