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Direct GPU Compilation and Execution for Host Applications with OpenMP Parallelism

Shilei Tian, Joseph Huber, Konstantinos Parasyris, Barbara Chapman, Johannes Doefert

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Tags: Computer science, CUDA, LLVM, nVidia, nVidia A100, OpenMP, Portability

September 11, 2022 by hgpu

EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models

Jiangsu Du, Ziming Liu, Jiarui Fang, Shenggui Li, Yongbin Li, Yutong Lu, Yang You

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, NLP, nVidia, nVidia A100, Package

September 11, 2022 by hgpu

Sgap: Towards Efficient Sparse Tensor Algebra Compilation for GPU

Genghan Zhang, Yuetong Zhao, Yanting Tao, Zhongming Yu, Guohao Dai, Sitao Huang, Yuan Wen, Pavlos Petoumenos, Yu Wang

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce RTX 2080, nVidia GeForce RTX 3090, Package, Sparse matrix, Tesla V100

September 11, 2022 by hgpu

GGArray: A Dynamically Growable GPU Array

Enzo meneses, Cristóbal A. Navarro, Héctor Ferrada

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Tags: Algorithms, Computer science, CUDA, Memory, nVidia, nVidia A100, nVidia Titan RTX

September 4, 2022 by hgpu

Understanding the Power of Evolutionary Computation for GPU Code Optimization

Jhe-Yu Liou, Muaaz Awan, Steven Hofmeyr, Stephanie Forrest, Carole-Jean Wu

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Tags: Bioinformatics, Biology, Computer science, CUDA, Evolutionary Computations, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTX 1080 Ti, Package, performance portability, Sequence alignment, Tesla P100, Tesla V100

September 4, 2022 by hgpu

Towards Understanding and Mitigating Memory-Access Challenges in Computing Systems

Mohammad K. Dashti

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Tags: Computer science, CUDA, Deep learning, Heterogeneous systems, Memory model, nVidia, nVidia GeForce RTX 2070, nVidia Jetson TK1, OpenCL, Package, Thesis

August 28, 2022 by hgpu

Fully Concurrent GPU Data Structures

Muhammad Abdelghaffar Awad

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Tags: Computer science, CUDA, Data Structures and Algorithms, nVidia, nVidia GeForce GTX Titan V, Package, Tesla V100

August 28, 2022 by hgpu

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer

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Tags: Computer science, CUDA, Deep learning, Machine learning, Matrix multiplication, nVidia, nVidia A40, Package, PyTorch

August 21, 2022 by hgpu

Optimization of GPU workloads using natural language processing based on deep learning techniques

Petros Vavaroutsos

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Tags: ATI, ATI Radeon HD 7970, Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 970, OpenCL, Thesis

August 21, 2022 by hgpu

A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

Jose González-Abad, Álvaro López García, Valentin Y. Kozlov

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Tags: Computer science, CUDA, Deep learning, HPC, nVidia, nVidia GeForce GTX 980 Ti, Package, Tesla V100

August 7, 2022 by hgpu

COX: Exposing CUDA Warp-Level Functions to CPUs

Ruobing Han, Jaewon Lee, Jaewoong Sim, Hyesoon Kim

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Tags: Benchmarking, Compilers, Computer science, CUDA, HIP, nVidia

August 7, 2022 by hgpu

Design and Implementation of ShenWei Universal C/C++

Huanqi Cao, Jiajie Chen

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Tags: Compilers, Computer science, CUDA, Heterogeneous systems, nVidia, OpenCL

August 7, 2022 by hgpu

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

Agentic Code Optimization via Compiler-LLM Cooperation

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high performance computing on graphics processing units: hgpu.org

Programming

Direct GPU Compilation and Execution for Host Applications with OpenMP Parallelism

EnergonAI: An Inference System for 10-100 Billion Parameter Transformer Models

Sgap: Towards Efficient Sparse Tensor Algebra Compilation for GPU

GGArray: A Dynamically Growable GPU Array

Understanding the Power of Evolutionary Computation for GPU Code Optimization

Towards Understanding and Mitigating Memory-Access Challenges in Computing Systems

Fully Concurrent GPU Data Structures

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Optimization of GPU workloads using natural language processing based on deep learning techniques

A Container-Based Workflow for Distributed Training of Deep Learning Algorithms in HPC Clusters

COX: Exposing CUDA Warp-Level Functions to CPUs

Design and Implementation of ShenWei Universal C/C++

Recent source codes

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning

Probe-and-Refine Tuning of Repository Guidance for AI Coding Agents

CUDAnalyst (CUDA + Analyst)

CodegenBench

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Agentic Code Optimization via Compiler-LLM Cooperation

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