high performance computing on graphics processing units: hgpu.org

hgpu.org » Machine learning

Deep Learning Based FPGA-CPU Acceleration

R. Luis Jalabert

View

Download (PDF)

Source codes

Tags: Code generation, Computer science, Deep learning, FPGA, Machine learning, Neural networks, Package, Thesis

November 17, 2019 by hgpu

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Tomasz Kornuta

View

Download (PDF)

Source codes

Tags: Computer science, Computer vision, Machine learning, Neural networks, Package, Python

October 27, 2019 by hgpu

Blink: Fast and Generic Collectives for Distributed ML

Guanhua Wang, Shivaram Venkataraman, Amar Phanishayee, Jorgen Thelin, Nikhil Devanur, Ion Stoica

View

Download (PDF)

Tags: Computer science, CUDA, Distributed computing, Heterogeneous systems, Machine learning, nVidia, nVidia DGX-1, Tesla V100

October 20, 2019 by hgpu

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Ilia Sivkov, Alfio Lazzaro, Jurg Hutter

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Data mining, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Signal processing, Sparse matrix, Tesla P100

October 20, 2019 by hgpu

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

Ameer M.S. Abdelhadi, Christos-Savvas Bouganis, George A. Constantinides

View

Download (PDF)

Source codes

Tags: Computer science, FPGA, Information Retrieval, Machine learning, Nearest neighbour, Package

October 13, 2019 by hgpu

Neural Network Inference on Mobile SoCs

Siqi Wang, Anuj Pathania, Tulika Mitra

View

Download (PDF)

Tags: ARM, Computer science, Heterogeneous systems, Machine learning, Neural networks, SoC

September 8, 2019 by hgpu

Demystifying the MLPerf Benchmark Suite

Snehil Verma, Qinzhe Wu, Bagus Hanindhito, Gunjan Jha, Eugene B. John, Ramesh Radhakrishnan, Lizy K. John

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Deep learning, Machine learning, nVidia, Package, Tesla P100, Tesla V100

September 1, 2019 by hgpu

Automated Architecture Design for Deep Neural Networks

Steven Abreu

View

Download (PDF)

Tags: Computer science, Deep learning, GPU cluster, Machine learning, Neural networks, nVidia, Tesla K80, Thesis

September 1, 2019 by hgpu

Survey and Benchmarking of Machine Learning Accelerators

Albert Reuther, Peter Michaleas, Michael Jones, Vijay Gadepally, Siddharth Samsi, Jeremy Kepner

View

Download (PDF)

Tags: ASIC, Benchmarking, Computer science, CUDA, FPGA, Machine learning, Neural networks, nVidia, nVidia Jetson TX1, nVidia Jetson TX2, Performance, TPU

September 1, 2019 by hgpu

Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth

Muhammad K.A. Hamdan, Diane Rover, John Just

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, Machine learning, Neural networks, Package

August 18, 2019 by hgpu

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

Mohammed Henni, Ilhem Isra Mekki

View

Download (PDF)

Tags: Code generation, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia DGX-1

August 11, 2019 by hgpu

GPU-Accelerated Atari Emulation for Reinforcement Learning

Steven Dalton, Iuri Frosio, Michael Garland

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX Titan V, Package, Tesla V100

July 28, 2019 by hgpu

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

Analyzing the Performance Portability of SYCL across CPUs, GPUs, and Hybrid Systems with Protein Database Search

tdg-benchs: benchmarks used to test the performance of taskgraph

Leveraging the potential of task-based programming with OpenMP task graphs

LLOR: Automatic Repair of OpenMP Programs

LLOR: Automated Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

Unified schemes for directive-based GPU offloading

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

PyOMP: Parallel programming for CPUs and GPUs with OpenMP and Python

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Deep Learning Based FPGA-CPU Acceleration

PyTorchPipe: a framework for rapid prototyping of pipelines combining language and vision

Blink: Fast and Generic Collectives for Distributed ML

DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems

Accelerated Approximate Nearest Neighbors Search Through Hierarchical Product Quantization

Neural Network Inference on Mobile SoCs

Demystifying the MLPerf Benchmark Suite

Automated Architecture Design for Deep Neural Networks

Survey and Benchmarking of Machine Learning Accelerators

Mass Estimation from Images using Deep Neural Network and Sparse Ground Truth

A Deep Learning Approach for Automatic Code Optimization in the Tiramisu Compiler

GPU-Accelerated Atari Emulation for Reinforcement Learning

Recent source codes

Matrix multiplication using Tensor Cores in CUDA

CPP Joules: Energy Measurement tool for CPP/CUDA programs

HPC-Coder-v2

Reproducible Study and Performance Analysis of GPU Programming Paradigms: OpenACC vs. CUDA in Key Linear Algebra Computations

SW#SYCL

tdg-benchs: benchmarks used to test the performance of taskgraph

LLOR: Automatic Repair of OpenMP Programs

Solomon: Simple Off-LOading Macros Orchestrating multiple Notations

CLUEstering: a high-performance density-based clustering library for scientific computing

PyOMP: OpenMP for Python in Numba

Most viewed papers (last 30 days)