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Improving Code Generation via Small Language Model-as-a-judge

Giuseppe Crupi, Rosalia Tufano, Gabriele Bavota

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Tags: Code generation, Computer science, LLM, nVidia, nVidia GeForce RTX 3090

February 16, 2026 by hgpu

Improving HPC Code Generation Capability of LLMs via Online Reinforcement Learning with Real-Machine Benchmark Rewards

Ryo Mikasa, Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino, Takahiro Katagiri

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Tags: Benchmarking, Code generation, Computer science, CUDA, HPC, LLM, Matrix multiplication, nVidia, nVidia H100, OpenMP, Performance

February 16, 2026 by hgpu

DICE: Diffusion Large Language Models Excel at Generating CUDA Kernels

Haolei Bai, Lingcheng Kong, Xueyi Chen, Jianmian Wang, Zhiqiang Tao, Huan Wang

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

February 16, 2026 by hgpu

Deep Kernel Fusion for Transformers

Zixi Zhang, Zhiwen Mo, Yiren Zhao, Robert Mullins

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Tags: Computer science, CUDA, LLM, nVidia, nVidia A100, Performance

February 16, 2026 by hgpu

HetCCL: Accelerating LLM Training with Heterogeneous GPUs

Heehoon Kim, Jaehwan Lee, Taejeoung Kim, Jongwon Park, Jinpyo Kim, Pyongwon Suh, Ryan H. Choi, Sangwoo Lee, Jaejin Lee

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Tags: AMD, AMD FirePro W7800, Computer science, Deep learning, GPU cluster, Heterogeneous systems, LLM, nVidia, Tesla V100

February 8, 2026 by hgpu

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

Wei Liu, Jiawei Xu, Yingru Li, Longtao Zheng, Tianjian Li, Qian Liu, Junxian He

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Tags: Computer science, CUDA, LLM, nVidia, nVidia H100, Package, Triton

February 8, 2026 by hgpu

Scaling GPU-to-CPU Migration for Efficient Distributed Execution on CPU Clusters

Ruobing Han, Hyesoon Kim

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Tags: Compilers, Computer science, CUDA, nVidia, nVidia A100, nVidia V100, Triton

February 8, 2026 by hgpu

Inside VOLT: Designing an Open-Source GPU Compiler (Tool)

Shinnung Jeong, Chihyo Ahn, Huanzhi Pu, Jisheng Zhao, Hyesoon Kim, Blaise Pascal Tine

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Tags: Compilers, Computer science, CUDA, FPGA, nVidia, OpenCL, Package

February 8, 2026 by hgpu

Private LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs

Jonathan Knoop, Hendrik Holtmann

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Tags: Cloud, Computer science, CUDA, LLM, nVidia, nVidia GeForce RTX 5060 Ti, nVidia GeForce RTX 5070 Ti, nVidia GeForce RTX 5090, Package

February 2, 2026 by hgpu

Nsight Python: A Python-First Profiling Toolkit for Seamless GPU Kernel Analysis (Tool)

Bastian Hagedorn, Alexander Collins, Tony Mongkolsmai, Vinod Grover

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Tags: Computer science, CUDA, nVidia, nVidia B200, Package, Performance, Profiling, Python, Triton

February 2, 2026 by hgpu

A Two-Stage GPU Kernel Tuner Combining Semantic Refactoring and Search-Based Optimization

Qiuyi Qu, Yicheng Sui, Yufei Sun, Rui Chen, Xiaofei Zhang, Yuzhi Zhang, Haofeng Wang, Ge Lan, Ning Zhang

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Tags: Computation and Language, Computer science, CUDA, LLM, nVidia, nVidia Titan RTX, OpenCL

January 25, 2026 by hgpu

Towards Automated Kernel Generation in the Era of LLMs

Yang Yu, Peiyu Zang, Chi Hsu Tsai, Haiming Wu, Yixin Shen, Jialing Zhang, Haoyu Wang, Zhiyou Xiao, Jingze Shi, Yuyu Luo, Wentao Zhang, Chunlei Men, Guang Liu, Yonghua Lin

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Tags: AMD, Benchmarking, Computer science, CUDA, LLM, nVidia, ROCm, Triton

January 25, 2026 by hgpu

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation

Introduction to Computer Vision

Introduction to Computer Vision

ACT: Accelerator Compiler Toolkit

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

pytest-gpu-proof: A Simple Pytest Extension for Local GPU Code Testing

pytest-gpu-proof: Enabling Cloud-CPU Continuous Integration for GPU Code with Local GPU Attestation

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

Accelerating the Mitigation of LLM Inference Nondeterminism Across GPU Architectures

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

AutoTuneBench: Trustworthy Measurement for Agent Auto-Tuning of LLM Serving Engines

nvsass-disassembler

Automated Instruction Encoding Synthesis for Modern GPU ISA Compression

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

FP4 FlashAttention forward, backward, and TorchTitan reproduction code

Hardware-Aware FP4 FlashAttention-4

EinsummableV3 - graph-based computation with tensor operations support

Every Kernel Is a Join: Automatic Multi-GPU Parallelism for AI Computations in Einsummable

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