high performance computing on graphics processing units: hgpu.org

hgpu.org » Benchmarking

Automatically Exploiting the Memory Hierarchy of GPUs through Just-in-Time Compilation

Michail Papadimitriou, Juan Fumero, Athanasios Stratikopoulos, Christos Kotselidis

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, Java, nVidia, nVidia GeForce GTX 1650, OpenCL, Package

May 23, 2021 by hgpu

NPBench: A Benchmarking Suite for High-Performance NumPy

Alexandros Nikolaos Ziogas, Tal Ben-Nun, Timo Schneider, Torsten Hoefler

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, HPC, Linear Algebra, nVidia, Package, Performance, Python, Tesla V100

May 16, 2021 by hgpu

PeriPy – A High Performance OpenCL Peridynamics Package

B. Boys, T. J. Dodwell, M. Hobbs, M. Girolami

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, Heterogeneous systems, nVidia, nVidia GeForce RTX 2080 Ti, OpenCL, Package, Python

May 16, 2021 by hgpu

Performance Evaluation and Improvements of the PoCL Open-Source OpenCL Implementation on Intel CPUs

Tobias Baumann, Matthias Noack, Thomas Steinke

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, LLVM, OpenCL, Package, Video

May 9, 2021 by hgpu

Sylkan: Towards a Vulkan Compute Target Platform for SYCL

Peter Thoman, Daniel Gogl, Thomas Fahringer

View

Download (PDF)

Tags: Benchmarking, Computer science, Data parallelism, Heterogeneous systems, OpenCL, SYCL, Video, Vulkan

May 9, 2021 by hgpu

Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

Hamid Tabani, Fabio Mazzocchetti, Pedro Benedicte, Jaume Abella, Francisco J. Cazorla

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, Performance

April 25, 2021 by hgpu

Under the Hood of SYCL – An Initial Performance Analysis With an Unstructured-mesh CFD Application

Istvan Z. Reguly, Andrew M.B. Owenson, Archie Powell, Stephen A. Jarvis, Gihan R. Mudalige

View

Download (PDF)

Source codes

Tags: Benchmarking, cfd, CUDA, Fluid dynamics, MPI, nVidia, OpenMP, Package, SYCL

April 18, 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

Porting a sparse linear algebra math library to Intel GPUs

Yuhsiang M. Tsai, Terry Cojean, Hartwig Anzt

View

Download (PDF)

Source codes

Tags: AMD Radeon VII, ATI, Benchmarking, Computer science, HIP, Linear Algebra, nVidia, Package, Performance, Sparse, Sparse matrix, Tesla V100

March 21, 2021 by hgpu

Benchmarking Modern Edge Devices for AI Applications

Pilsung Kang, Jongmin Jo

View

Download (PDF)

Tags: Artificial intelligence, Benchmarking, Computer science, Deep learning, TPU

March 7, 2021 by hgpu

GPU-aware Communication with UCX in Parallel Programming Models: Charm++, MPI, and Python

Jaemin Choi, Zane Fink, Sam White, Nitin Bhat, David F. Richards, Laxmikant V. Kale

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, CUDA, Distributed computing, MPI, nVidia, Package, Python, Tesla V100

February 28, 2021 by hgpu

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Jiří Filipovič, Jana Hozzová, Amin Nezarat, Jaroslav Oľha, Filip Petrovič

View

Download (PDF)

Source codes

Tags: Auto-Tuning, Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 1070, nVidia GeForce GTX 680, nVidia GeForce GTX 750, nVidia GeForce RTX 2080, Package

February 14, 2021 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

* * *

high performance computing on graphics processing units: hgpu.org

Automatically Exploiting the Memory Hierarchy of GPUs through Just-in-Time Compilation

NPBench: A Benchmarking Suite for High-Performance NumPy

PeriPy – A High Performance OpenCL Peridynamics Package

Performance Evaluation and Improvements of the PoCL Open-Source OpenCL Implementation on Intel CPUs

Sylkan: Towards a Vulkan Compute Target Platform for SYCL

Performance Analysis and Optimization Opportunities for NVIDIA Automotive GPUs

Under the Hood of SYCL – An Initial Performance Analysis With an Unstructured-mesh CFD Application

LS-CAT: A Large-Scale CUDA AutoTuning Dataset

Porting a sparse linear algebra math library to Intel GPUs

Benchmarking Modern Edge Devices for AI Applications

GPU-aware Communication with UCX in Parallel Programming Models: Charm++, MPI, and Python

Searching CUDA code autotuning spaces with hardware performance counters: data from benchmarks running on various GPU architectures

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

Most viewed papers (last 30 days)