high performance computing on graphics processing units: hgpu.org

hgpu.org » nVidia GeForce GTX 980

Improving Energy Efficiency of Basic Linear Algebra Routines on Heterogeneous Systems with Multiple GPUs

Hadi Zamani Sabzi

View

Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Factorization, Heterogeneous systems, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 1660, nVidia GeForce GTX 980, Thesis

May 21, 2023 by hgpu

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Manuel Costanzo, Enzo Rucci, Carlos García Sánchez, Marcelo Naiouf, Manuel Prieto-Matías

View

Download (PDF)

Source codes

Tags: Bioinformatics, Biology, Computational biology, Computer science, CUDA, Heterogeneous systems, Intel, Intel UHD 630, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, nVidia GeForce RTX 2070, nVidia GeForce RTX 3090, OpenCL, Sequence alignment, SYCL, Tesla V100

November 27, 2022 by hgpu

Novel Parallel Approaches to Efficiently Solve Spatial Problems on Heterogeneous CPU-GPU Systems

Andrés Jesús Sánchez Fernández

View

Download (PDF)

Tags: Algorithms, Computer science, Databases, Heterogeneous systems, nVidia, nVidia GeForce GT 1030, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 980, Thesis

July 3, 2022 by hgpu

BAT: A Benchmark suite for AutoTuners

Ingunn Sund, Knut A. Kirkhorn, Jacob O. Tørring, Anne C. Elster

View

Download (PDF)

Source codes

Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 980, Package, Performance, Tesla T4, Tesla V100

November 21, 2021 by hgpu

LS-CAT: A Large-Scale CUDA AutoTuning Dataset

Lars Bjertnes, Jacob O. Tørring, Anne C. Elster

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, Databases, Linear Algebra, Machine learning, nVidia, nVidia GeForce GTX 980, Tesla T4

April 5, 2021 by hgpu

Acceleration of Intrusion Detection in Encrypted Network Traffic Using Heterogeneous Hardware

Eva Papadogiannaki, Sotiris Ioannidis

View

Download (PDF)

Tags: Computer science, Filtering, Heterogeneous systems, Intel UHD 630, nVidia, nVidia GeForce GTX 980, OpenCL, Security

February 28, 2021 by hgpu

Efficient Nearest-Neighbor Data Sharing in GPUs

Negin Nematollahi, Mohammad Sadrosadati, Hajar Falahati, Marzieh Barkhordar, Mario Paulo Drumond, Hamid Sarbazi-Azad, Babak Falsafi

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Stencil computation

January 10, 2021 by hgpu

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Shaohuai Shi, Qiang Wang, Xiaowen Chu

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUBLAS, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Package, Sparse matrix, Tesla P100

June 7, 2020 by hgpu

Memory-Efficient Object-Oriented Programming on GPUs

Matthias Springer

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 940 M, nVidia GeForce GTX 980, nVidia GeForce GTX Titan XP, Package, Programming Languages, Thesis

August 25, 2019 by hgpu

Automatic generation of warp-level primitives and atomic instructions for fast and portable parallel reduction on GPUs

Simon Garcia De Gonzalo, Sitao Huang, Juan Gomez-Luna, Simon Hammond, Onur Mutlu, Wen-mei Hwu

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 980, Performance, performance portability, Tesla K40, Tesla P100

May 26, 2019 by hgpu

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Shaohuai Shi, Qiang Wang, Xiaowen Chu, Bo Li

View

Download (PDF)

Source codes

Tags: Artificial intelligence, Benchmarking, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 1080, nVidia GeForce GTX 980, Package, Performance, Tesla K80

May 12, 2018 by hgpu

Portable Real-Time DCT Based Steganography Using OpenCL

Ante Poljicak, Guillermo Botella, Carlos Garcia, Luka Kedmenec, Manuel Prieto-Matias

View

Download (PDF)

Tags: APU, ARM, ATI, ATI Radeon HD 6870, Image processing, nVidia, nVidia GeForce GTX 980, OpenCL, real time systems

March 10, 2018 by hgpu

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

Experiences in Building a Composable and Functional API for Runtime SPIR-V Code Generation

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

TorchBench: Benchmarking PyTorch with High API Surface Coverage

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Descend: A Safe GPU Systems Programming Language

Redwood heterogeneous framework

Redwood: Flexible and Portable Heterogeneous Tree Traversal Workloads

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Improving Energy Efficiency of Basic Linear Algebra Routines on Heterogeneous Systems with Multiple GPUs

Assessing Opportunities of SYCL and Intel oneAPI for Biological Sequence Alignment

Novel Parallel Approaches to Efficiently Solve Spatial Problems on Heterogeneous CPU-GPU Systems

BAT: A Benchmark suite for AutoTuners

LS-CAT: A Large-Scale CUDA AutoTuning Dataset

Acceleration of Intrusion Detection in Encrypted Network Traffic Using Heterogeneous Hardware

Efficient Nearest-Neighbor Data Sharing in GPUs

Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format

Memory-Efficient Object-Oriented Programming on GPUs

Automatic generation of warp-level primitives and atomic instructions for fast and portable parallel reduction on GPUs

Modeling and Evaluation of Synchronous Stochastic Gradient Descent in Distributed Deep Learning on Multiple GPUs

Portable Real-Time DCT Based Steganography Using OpenCL

Recent source codes

Genomics-GPU: A Benchmark Suite for GPU-accelerated Genome Analysis

AutoDock for GPUs and other accelerators

spotPython: Sequential Parameter Optimization in Python

Test benchmarks for CELERITY

Dragon-Alpha&cu32: A Java-based Tensor Computing Framework With its High-Performance CUDA Library

Beehive SPIR-V Toolkit

TorchBench: a collection of open source benchmarks used to evaluate PyTorch performance

Prediction of Performance and Power Consumption of GPGPU Applications

Descend: an attempt at safe imperative GPU programming

Redwood heterogeneous framework

Most viewed papers (last 30 days)