high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

Auto-tuning Streamed Applications on Intel Xeon Phi

Peng Zhang, Jianbin Fang, Tao Tang, Canqun Yang, Zheng Wang

View

Download (PDF)

Source codes

Tags: Computer science, Intel Xeon Phi, Machine learning, Package, Performance

May 5, 2018 by hgpu

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Joseph D. Garvey, Tarek S. Abdelrahman

View

Download (PDF)

Tags: AMD Radeon R9 390, ATI, Computer science, Machine learning, nVidia, nVidia GeForce GTX Titan, OpenCL, Performance, Stencil computation

April 28, 2018 by hgpu

Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking

Zhe Jia, Marco Maggioni, Benjamin Staiger, Daniele P. Scarpazza

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, nVidia, Performance, PTX, Tesla V100

April 22, 2018 by hgpu

Automatic Optimization of OpenCL-Based Stencil Codes for FPGAs and Its Evaluation

Tsukasa Endo, Hasitha Muthumala Waidyasooriya, Masanori Hariyama

View

Download (PDF)

Tags: Computer science, FPGA, OpenCL, Performance, Stencil computation

April 15, 2018 by hgpu

A Comparison between GPU-based Volume Ray Casting Implementations: Fragment Shader, Compute Shader, OpenCL, and CUDA

Francisco Sans, Rhadames Carmona

View

Download (PDF)

Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 660, OpenCL, OpenGL, Performance, Rendering

March 31, 2018 by hgpu

Scalable Breadth-First Search on a GPU Cluster

Yuechao Pan, Roger Pearce, John D. Owens

View

Download (PDF)

Tags: Computer science, CUDA, GPU cluster, nVidia, OpenMP, Performance, Tesla P100

March 25, 2018 by hgpu

NVIDIA Tensor Core Programmability, Performance & Precision

Stefano Markidis, Steven Wei Der Chien, Erwin Laure, Ivy Bo Peng, Jeffrey S. Vetter

View

Download (PDF)

Tags: Computer science, CUDA, Mixed precision, nVidia, Performance, Tesla V100

March 17, 2018 by hgpu

Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs

Moritz Kreutzer, Georg Hager, Dominik Ernst, Holger Fehske, Alan R. Bishop, Gerhard Wellein

View

Download (PDF)

Source codes

Tags: Chemistry, Computational Physics, Computer science, CUDA, Intel Xeon Phi, nVidia, OpenMP, Package, Performance, Physics, Quantum Physics, Tesla P100

March 10, 2018 by hgpu

Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers

Riyadh Baghdadi, Jessica Ray, Malek Ben Romdhane, Emanuele Del Sozzo, Patricia Suriana, Shoaib Kamil, Saman Amarasinghe

View

Download (PDF)

Tags: Compilers, Computer science, DSL, FPGA, Matrix multiplication, nVidia, OpenMPI, Performance, Programming Languages, PTX, Tesla K40

March 3, 2018 by hgpu

OpenCL Acceleration for TensorFlow

Mehdi Goli, Luke Iwanski, John Lawson, Uwe Dolinsky, Andrew Richards

View

Download (PDF)

Tags: ARM, Computer science, Deep learning, Machine learning, OpenCL, Performance, SYCL, TensorFlow

March 3, 2018 by hgpu

The performances of R GPU implementations of the GMRES method

Bogdan Oancea, Richard Pospisil

View

Download (PDF)

Tags: Computer science, CUDA, Linear Algebra, nVidia, nVidia GeForce GTX 840 M, OpenCL, Performance, R

February 17, 2018 by hgpu

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

Philippe Tillet, David Cox

View

Download (PDF)

Source codes

Tags: Auto-Tuning, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 980 Ti, OpenCL, Package, Performance, PTX, Tesla P100

February 17, 2018 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Auto-tuning Streamed Applications on Intel Xeon Phi

A Strategy for Automatic Performance Tuning of Stencil Computations on GPUs

Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking

Automatic Optimization of OpenCL-Based Stencil Codes for FPGAs and Its Evaluation

A Comparison between GPU-based Volume Ray Casting Implementations: Fragment Shader, Compute Shader, OpenCL, and CUDA

Scalable Breadth-First Search on a GPU Cluster

NVIDIA Tensor Core Programmability, Performance & Precision

Chebyshev Filter Diagonalization on Modern Manycore Processors and GPGPUs

Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding Hardware Complexity from DSL Compilers

OpenCL Acceleration for TensorFlow

The performances of R GPU implementations of the GMRES method

Input-Aware Auto-Tuning of Compute-Bound HPC Kernels

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

Most viewed papers (last 30 days)