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LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning

Neha Prakriya, Zijian Ding, Yizhou Sun, Jason Cong

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Tags: AMD Radeon Instinct MI250, ATI, Computer science, FPGA, HLS, LLM, Machine learning, Neural networks, PyTorch

May 4, 2025 by hgpu

Scaling On-Device GPU Inference for Large Generative Models

Jiuqiang Tang, Raman Sarokin, Ekaterina Ignasheva, Grant Jensen, Lin Chen, Juhyun Lee, Andrei Kulik, Matthias Grundmann

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Tags: AI, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia GeForce RTX 4090, OpenCL

May 4, 2025 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

Weijie Lv, Xuan Xia, Sheng-Jun Huang

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, Package, Python, PyTorch

April 27, 2025 by hgpu

Scalability Evaluation of HPC Multi-GPU Training for ECG-based LLMs

Dimitar Mileski, Nikola Petrovski, Marjan Gusev

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Tags: Computer science, CUDA, Deep learning, HPC, LLM, nVidia, nVidia A100, PTX, PyTorch

April 13, 2025 by hgpu

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Meng Wang, Jinshuo Liu, Zimeng Xu, Chang Ding, Xiaomeng Huang

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Tags: Code generation, Computer science, CUDA, LLM, nVidia, nVidia GeForce RTX 4090, Package

April 13, 2025 by hgpu

Efficient allocation of image recognition and LLM tasks on multi-GPU system

Marcin Lawenda, Krzesimir Samborski, Kyrylo Khloponin, Łukasz Szustak

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Tags: Computer science, CUDA, Data parallelism, Image recognition, LLM, Machine learning, nVidia, nVidia H100, Performance

March 30, 2025 by hgpu

TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives

Size Zheng, Jin Fang, Xuegui Zheng, Qi Hou, Wenlei Bao, Ningxin Zheng, Ziheng Jiang, Dongyang Wang, Jianxi Ye, Haibin Lin, Li-Wen Chang, Xin Liu

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Tags: Computer science, Deep learning, LLM, nVidia, nVidia H800, PTX

March 30, 2025 by hgpu

LLMPerf: GPU Performance Modeling meets Large Language Models

Khoi N.M. Nguyen, Hoang Duy Nguyen Do, Huyen Thao Le, Thanh Tuan Dao

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Tags: Computer science, LLM, Machine learning, nVidia, OpenCL, Performance, Tesla V100

March 23, 2025 by hgpu

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

Jianling Li, Shangzhan Li, Zhenye Gao, Qi Shi, Yuxuan Li, Zefan Wang, Jiacheng Huang, Haojie Wang, Jianrong Wang, Xu Han, Zhiyuan Liu, Maosong Sun

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Tags: Benchmarking, Code generation, Computer science, CUDA, Deep learning, LLM, nVidia, nVidia A100, Package, Python

March 3, 2025 by hgpu

KernelBench: Can LLMs Write Efficient GPU Kernels?

Anne Ouyang, Simon Guo, Simran Arora, Alex L. Zhang, William Hu, Christopher Ré, Azalia Mirhoseini

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Tags: AI, Benchmarking, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia L40s, Package, PyTorch

February 24, 2025 by hgpu

Evaluating the Performance of the DeepSeek Model in Confidential Computing Environment

Ben Dong, Qian Wang

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Tags: Benchmarking, Cloud, Computer science, LLM, nVidia, nVidia A100, Performance, Security

February 24, 2025 by hgpu

The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition

Robert Tjarko Lange, Aaditya Prasad, Qi Sun, Maxence Faldor, Yujin Tang, David Ha

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Tags: AI, Computer science, CUDA, LLM, nVidia, nVidia H100, Package, Performance

February 24, 2025 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

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high performance computing on graphics processing units: hgpu.org

LIFT: LLM-Based Pragma Insertion for HLS via GNN Supervised Fine-Tuning

Scaling On-Device GPU Inference for Large Generative Models

Data-efficient LLM Fine-tuning for Code Generation

Scalability Evaluation of HPC Multi-GPU Training for ECG-based LLMs

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

Efficient allocation of image recognition and LLM tasks on multi-GPU system

TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives

LLMPerf: GPU Performance Modeling meets Large Language Models

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators

KernelBench: Can LLMs Write Efficient GPU Kernels?

The AI CUDA Engineer: Agentic CUDA Kernel Discovery, Optimization and Composition

Recent source codes

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

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