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Kevin: Multi-Turn RL for Generating CUDA Kernels

Carlo Baronio, Pietro Marsella, Ben Pan, Simon Guo, Silas Alberti

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Tags: Artificial intelligence, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia H100, nVidia H200, Performance

July 20, 2025 by hgpu

Dissecting the NVIDIA Blackwell Architecture with Microbenchmarks

Aaron Jarmusch, Nathan Graddon, Sunita Chandrasekaran

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Tags: Benchmarking, Computer science, CUDA, HPC, nVidia, nVidia GeForce RTX 5080, nVidia H100, Performance, PTX

July 20, 2025 by hgpu

Omniwise: Predicting GPU Kernels Performance with LLMs

Zixian Wang, Cole Ramos, Muhammad A. Awad, Keith Lowery

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Tags: AMD, AMD Radeon Instinct MI250, AMD Radeon Instinct MI300X, Artificial intelligence, Benchmarking, Computer science, LLM, Neural networks, Performance, ROCm

June 29, 2025 by hgpu

GCStack+GCScaler: Fast and Accurate GPU Performance Analyses Using Fine-Grained Stall Cycle Accounting and Interval Analysis

Hanna Cha, Sungchul Lee, Jounghoo Lee, Yeonan Ha, Joonsung Kim, Youngsok Kim

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Tags: Benchmarking, Computer science, CUDA, Design space exploration, nVidia, nVidia GeForce RTX 3070, Performance

June 29, 2025 by hgpu

All You Need Is Binary Search! A Practical View on Lightweight Database Indexing on GPUs

Justus Henneberg, Felix Schuhknecht

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Tags: Computer science, CUDA, Databases, nVidia, nVidia RTX A6000, Performance

June 8, 2025 by hgpu

Performance of Confidential Computing GPUs

Antonio Martínez Ibarra, Julian James Stephen, Aurora González Vidal, K. R. Jayaram, Antonio Fernando Skarmeta Gómez

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Tags: Computer science, CUDA, LLM, nVidia, nVidia H100, Performance, Security

May 25, 2025 by hgpu

Low-cost edge computing using upcycled smartphones

Corentin Libert

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Tags: Computer science, Package, Performance, TensorFlow, Thesis

May 25, 2025 by hgpu

Comparing Parallel Functional Array Languages: Programming and Performance

David van Balen, Tiziano De Matteis, Clemens Grelck, Troels Henriksen, Aaron W. Hsu, Gabriele K. Keller, Thomas Koopman, Trevor L. McDonell, Cosmin Oancea, Sven-Bodo Scholz, Artjoms Sinkarovs, Tom Smeding, Phil Trinder, Ivo Gabe de Wolff, Alexandros Nikolaos Ziogas

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Tags: Benchmarking, Computer science, CUDA, HIP, N-body simulation, nVidia, nVidia A30, OpenCL, Package, Performance, performance portability, Programming Languages

May 18, 2025 by hgpu

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Zhonggen Li, Xiangyu Ke, Yifan Zhu, Yunjun Gao, Feifei Li

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia A100, Package, Performance, Prefetch

May 18, 2025 by hgpu

Can Large Language Models Predict Parallel Code Performance?

Gregory Bolet, Giorgis Georgakoudis, Harshitha Menon, Konstantinos Parasyris, Niranjan Hasabnis, Hayden Estes, Kirk W. Cameron, Gal Oren

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Tags: Benchmarking, Computer science, CUDA, LLM, nVidia, nVidia GeForce RTX 3080, OpenMP, Package, Performance, performance portability

May 18, 2025 by hgpu

GPU Performance Portability needs Autotuning

Burkhard Ringlein, Thomas Parnell, Radu Stoica

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Tags: AMD Radeon Instinct MI250, ATI, Auto-Tuning, Computer science, CUDA, DSL, HIP, LLM, nVidia, nVidia A100, Performance, performance portability

May 18, 2025 by hgpu

Exploration of Cryptocurrency Mining-Specific GPUs in AI Applications: A Case Study of CMP 170HX

Xing Kangwei

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Tags: AI, Artificial intelligence, Benchmarking, Computer science, CUDA, Finance, nVidia, nVidia CMP 170HX, OpenCL, Performance

May 18, 2025 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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