high performance computing on graphics processing units: hgpu.org

David Koeplinger, Matthew Feldman, Raghu Prabhakar, Yaqi Zhang, Stefan Hadjis, Ruben Fiszel, Tian Zhao, Luigi Nardi, Ardavan Pedram, Christos Kozyrakis, Kunle Olukotun

View

Download (PDF)

Source codes

Tags: Compilers, Computer science, Deep learning, DSL, FPGA, HLS, Machine learning, Package, Scala

July 21, 2018 by hgpu

Abelian: A Compiler for Graph Analytics on Distributed, Heterogeneous Platforms

Gurbinder Gill, Roshan Dathathri, Loc Hoang, Andrew Lenharth, Keshav Pingali

View

Download (PDF)

Tags: Computer science, CUDA, Graph theory, Intel Xeon Phi, nVidia, Tesla K80

July 21, 2018 by hgpu

LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks

Daniel H. Noronha, Bahar Salehpour, Steven J.E. Wilton

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, FPGA, LSTM, Machine learning, Neural networks, Package, Python, TensorFlow

July 21, 2018 by hgpu

Revisiting Online Autotuning for Sparse-Matrix Vector Multiplication Kernels on High-Performance Accelerators

Simon Garcia De Gonzalo, Wen-Mei Hwu, Simon D. Hammond, Christian R. Trott

View

Download (PDF)

Tags: Computer science, Intel Xeon Phi, nVidia, Performance, Sparse matrix, Tesla P100

July 15, 2018 by hgpu

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Dr. Kernel: Reinforcement Learning Done Right for Triton Kernel Generations

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Generating Literature-Driven Scientific Theories at Scale

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Scheduling Computation Graphs of Deep Learning Models on Manycore CPUs

Energy-based Tuning of Convolutional Neural Networks on Multi-GPUs

GPU schedulers: how fair is fair enough?

CRUM: Checkpoint-Restart Support for CUDA’s Unified Memory

Elementary functions: towards automatically generated, efficient, and vectorizable implementations

Optimization of OpenCL applications on FPGA

gSMat: A Scalable Sparse Matrix-based Join for SPARQL Query Processing

Block-Size Independence for GPU Programs

Spatial: A Language and Compiler for Application Accelerators

Abelian: A Compiler for Graph Analytics on Distributed, Heterogeneous Platforms

LeFlow: Enabling Flexible FPGA High-Level Synthesis of Tensorflow Deep Neural Networks

Revisiting Online Autotuning for Sparse-Matrix Vector Multiplication Kernels on High-Performance Accelerators

Recent source codes

CL4SE: A Context Learning Benchmark For Software Engineering Tasks

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Execution-Free Reward Models

A Safety Report on GPT-5.2, Gemini 3 Pro, Qwen3-VL, Grok 4.1 Fast, Nano Banana Pro, and Seedream 4.5

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

KernelGYM & Dr. Kernel: A distributed GPU environment and a collection of RL training methods to support RL for Kernel Generations

Vortex-Optimized Light-weight Toolchain (VOLT)

SciDef: Automated Definition Extraction from Scientific Literature

bioagent-bench: Benchmark for evaluating LLM agents in bioinformatics

Benchmark suite for LLM inference on NVIDIA consumer GPUs

Theorizer: from the paper Generating Literature-Driven Scientific Discoveries at Scale

Most viewed papers (last 30 days)