high performance computing on graphics processing units: hgpu.org

Applications

hgpu.org » Applications » Computer science

Transparent Compiler and Runtime Specializations for Accelerating Managed Languages on FPGAs

Michail Papadimitriou, Juan Fumero, Athanasios Stratikopoulos, Foivos S. Zakkak, Christos Kotselidis

View

Download (PDF)

Source codes

Tags: Computer science, FPGA, HLS, Java, OpenCL, Optimization, Package, Programming Languages

November 8, 2020 by hgpu

AMGCL – A C++ library for efficient solution of large sparse linear systems

Denis Demidov

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Mathematical Software, MPI, OpenCL, Package, Sparse, Sparse linear iterative solvers

November 8, 2020 by hgpu

TopicBERT for Energy Efficient Document Classification

Yatin Chaudhary, Pankaj Gupta, Khushbu Saxena, Vivek Kulkarni, Thomas Runkler, Hinrich Schütze

View

Download (PDF)

Tags: classification, Computer science, CUDA, Deep learning, NLP, nVidia, Tesla T4

November 8, 2020 by hgpu

Designing a Modern Skeleton Programming Framework for Parallel and Heterogeneous Systems

August Ernstsson

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, HPC, nVidia, OpenCL, Tesla K20, Thesis

November 1, 2020 by hgpu

Towards Co-execution on Commodity Heterogeneous Systems: Optimizations for Time-Constrained Scenarios

Raúl Nozal, Jose Luis Bosque, Ramon Beivide

View

Download (PDF)

Source codes

Tags: AMD Radeon R7, ATI, Computer science, Heterogeneous systems, load balancing, nVidia, nVidia GeForce GTX 950, OpenCL, Package, performance portability, Task scheduling

November 1, 2020 by hgpu

Out-of-core Training for Extremely Large-Scale Neural Networks With Adaptive Window-Based Scheduling

Akio Hayakawa, Takuya Narihira

View

Download (PDF)

Source codes

Tags: Algorithms, Computer science, CUDA, Deep learning, Neural networks, nVidia, Package, Tesla V100

November 1, 2020 by hgpu

Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks

John Brennan, Stephen Bonner, Amir Atapour-Abarghouei, Philip T Jackson, Boguslaw Obara, Andrew Stephen McGough

View

Download (PDF)

Tags: CNN, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia Titan RTX, PyTorch, Tesla P100, Tesla V100

November 1, 2020 by hgpu

Memory Optimization for Deep Networks

Aashaka Shah, Chao-Yuan Wu, Jayashree Mohan, Vijay Chidambaram, Philipp Krähenbühl

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Deep learning, Memory, nVidia, Optimization, Package, PyTorch, Tesla P100

November 1, 2020 by hgpu

OpenCL Performance on the Intel Heterogeneous Architecture Research Platform

Steven Harris, Roger D. Chamberlain, Christopher Gill

View

Download (PDF)

Tags: Computer science, FPGA, Heterogeneous systems, Matrix multiplication, OpenCL, Optimization, performance portability

October 25, 2020 by hgpu

Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

Joshua Hoke Davis, Christopher Daley, Swaroop Pophale, Thomas Huber, Sunita Chandrasekaran, Nicholas J. Wright

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, OpenMP, Performance, Tesla V100

October 25, 2020 by hgpu

Mixed-Precision Embedding Using a Cache

Jie (Amy)Yang, Jianyu Huang, Jongsoo Park, Ping Tak Peter Tang, Andrew Tulloch

View

Download (PDF)

Tags: Artificial intelligence, Computer science, CUDA, Machine learning, Mixed precision, nVidia, Tesla V100

October 25, 2020 by hgpu

When HLS Meets FPGA HBM: Benchmarking and Bandwidth Optimization

Young-kyu Choi, Yuze Chi, Jie Wang, Licheng Guo, Jason Cong

View

Download (PDF)

Tags: Benchmarking, Computer science, FPGA, HLS, OpenCL, Optimization

October 18, 2020 by hgpu

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

Experiences with implementing Kokkos’ SYCL backend

ROCm's implementation of Gromacs

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Applications

Transparent Compiler and Runtime Specializations for Accelerating Managed Languages on FPGAs

AMGCL – A C++ library for efficient solution of large sparse linear systems

TopicBERT for Energy Efficient Document Classification

Designing a Modern Skeleton Programming Framework for Parallel and Heterogeneous Systems

Towards Co-execution on Commodity Heterogeneous Systems: Optimizations for Time-Constrained Scenarios

Out-of-core Training for Extremely Large-Scale Neural Networks With Adaptive Window-Based Scheduling

Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks

Memory Optimization for Deep Networks

OpenCL Performance on the Intel Heterogeneous Architecture Research Platform

Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

Mixed-Precision Embedding Using a Cache

When HLS Meets FPGA HBM: Benchmarking and Bandwidth Optimization

Recent source codes

Code examples for paper on SYCL backend of Kokkos - IWOCL 2024

ROCm's implementation of Gromacs

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

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Most viewed papers (last 30 days)