high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Computer science

dMath: Distributed Linear Algebra for DL

Steven Eliuk, Cameron Upright, Hars Vardhan, Stephen Walsh, Trevor Gale

View

Tags: Algorithms, Computer science, CUDA, Deep learning, Linear Algebra, Matrix multiplication, MPI, Neural networks, nVidia, Tesla K80

November 25, 2016 by hgpu

Performance Analysis of CUDA and OpenCL By Implementation of Cryptographic Algorithms

Manas Mahapatra

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GT 525 M, OpenCL, Performance, Security, Thesis

November 23, 2016 by hgpu

A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications

Matthew W. Moskewicz, Ali Jannesari, Kurt Keutzer

View

Download (PDF)

Source codes

Tags: AMD R9 Nano, ARM, ATI, Code generation, Computer science, Computer vision, CUDA, Deep learning, Neural networks, nVidia, nVidia GeForce GTX Titan X, OpenCL, Package

November 23, 2016 by hgpu

Deep Tensor Convolution on Multicores

David Budden, Alexander Matveev, Shibani Santurkar, Shraman Ray Chaudhuri, Nir Shavit

View

Download (PDF)

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

November 23, 2016 by hgpu

GA3C: GPU-based A3C for Deep Reinforcement Learning

Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree, Jason Clemons, Jan Kautz

View

Download (PDF)

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

November 23, 2016 by hgpu

Processing OLTP Workloads on Hybrid CPU/GPU Systems

Mudit Bhatnagar

View

Download (PDF)

Tags: Computer science, Databases, FPGA, Heterogeneous systems, nVidia, nVidia GeForce GT 640, nVidia GeForce GTX Titan, OpenCL, Thesis

November 22, 2016 by hgpu

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

Ao Ren, Ji Li, Zhe Li, Caiwen Ding, Xuehai Qian, Qinru Qiu, Bo Yuan, Yanzhi Wang

View

Download (PDF)

Tags: ASIC, CNN, Computer science, Deep learning, FPGA, Neural networks, nVidia, Tesla C2075

November 22, 2016 by hgpu

Evaluation of an OpenCL-Based FPGA Platform for Particle Filter

Shunsuke Tatsumi, Masanori Hariyama, Norikazu Ikoma

View

Download (PDF)

Tags: Computer science, FPGA, OpenCL, Particle filtering

November 19, 2016 by hgpu

HIPAcc: A Domain-Specific Language and Compiler for Image Processing

Richard Membarth, Oliver Reiche, Frank Hannig, Jurgen Teich, Mario Korner, Wieland Eckert

View

Download (PDF)

Source codes

Tags: Algorithms, AMD Radeon R9 290X, ARM, ATI, Compilers, Computer science, CUDA, Heterogeneous systems, Image processing, Intel Xeon Phi, nVidia, OpenCL, Package, Tesla K20

November 19, 2016 by hgpu

How to scale distributed deep learning?

Peter H. Jin, Qiaochu Yuan, Forrest Iandola, Kurt Keutzer

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, GPU cluster, nVidia, Performance, Tesla K20, Tesla K80

November 19, 2016 by hgpu

Performance Analysis of Parallel Sorting Algorithms using GPU Computing

Neetu Faujdar, SP Ghrera

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 460, Performance, Sorting

November 19, 2016 by hgpu

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Pedro Bruel, Marcos Amaris, Alfredo Goldman

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 750, nVidia GeForce GTX 980, Package, Performance, Tesla K40

November 16, 2016 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Applications

dMath: Distributed Linear Algebra for DL

Performance Analysis of CUDA and OpenCL By Implementation of Cryptographic Algorithms

A Metaprogramming and Autotuning Framework for Deploying Deep Learning Applications

Deep Tensor Convolution on Multicores

GA3C: GPU-based A3C for Deep Reinforcement Learning

Processing OLTP Workloads on Hybrid CPU/GPU Systems

SC-DCNN: Highly-Scalable Deep Convolutional Neural Network using Stochastic Computing

Evaluation of an OpenCL-Based FPGA Platform for Particle Filter

HIPAcc: A Domain-Specific Language and Compiler for Image Processing

How to scale distributed deep learning?

Performance Analysis of Parallel Sorting Algorithms using GPU Computing

Autotuning CUDA Compiler Parameters for Heterogeneous Applications using the OpenTuner Framework

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Most viewed papers (last 30 days)