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Creating a Dataset for High-Performance Computing Code Translation using LLMs: A Bridge Between OpenMP Fortran and C+

Bin Lei, Caiwen Ding, Le Chen, Pei-Hung Lin, Chunhua Liao

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Tags: Benchmarking, Computer science, CUDA, Fortran, Machine learning, nVidia, nVidia RTX A6000, OpenMP, Package

November 19, 2023 by hgpu

Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPU

Mohammad Zubair, Christoph Bauinger

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Tags: Computer science, CUDA, Deep learning, Intel, Intel Data Center GPU Max 1550, Linear Algebra, Machine learning, Mathematical Software, Matrix multiplication, nVidia, nVidia V100, Sparse matrix, SYCL

November 5, 2023 by hgpu

Performance Tuning for GPU-Embedded Systems: Machine-Learning-based and Analytical Model-driven Tuning Methodologies

Adrian Perez Dieguez, Margarita Amor Lopez

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Tags: Computer science, CUDA, FFT, Machine learning, nVidia, nVidia Jetson TX1, Performance, performance portability

October 29, 2023 by hgpu

GEVO-ML: Optimizing Machine Learning Code with Evolutionary Computation

Jhe-Yu Liou, Stephanie Forrest, Carole-Jean Wu

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Tags: Computer science, CUDA, Deep learning, Evolutionary Computations, Machine learning, Neural and Evolutionary Computing, nVidia, nVidia P100

October 29, 2023 by hgpu

Predicting the Execution Time of a kernel on a specific GPU using PTX code

Monika Dagar, Jorge Roldan

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Tags: Computer science, CUDA, Deep learning, Machine learning, nVidia, nVidia GeForce GTX 1650, nVidia GeForce GTX Titan XP, Performance, PTX, Tesla K20, Tesla K80, Tesla M60, Tesla P100, Tesla T4, Tesla V100

October 22, 2023 by hgpu

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

Peter Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio, Anton Gorenko, Michael M. Henry, Frank Hu, Jing Huang, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João PGLM Rodrigues, Jaime Rodriguez-Guerra, Andrew C. Simmonett, Jason Swails, Ivy Zhang, John D. Chodera, Gianni De Fabritiis, Thomas E. Markland

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Tags: AMD Radeon Pro V620, ATI, Chemical Physics, CUDA, HIP, Machine learning, Molecular dynamics, Molecular simulation, nVidia, nVidia A100, nVidia GeForce RTX 4080, OpenCL, Package, Physics

October 15, 2023 by hgpu

Memory Efficient Mixed-Precision Optimizers

Basile Lewandowski, Atli Kosson

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Tags: Computer science, CUDA, Machine learning, Mixed precision, nVidia, nVidia A100, nVidia V100

October 1, 2023 by hgpu

Novel Parallelization Strategies for High-Performance DNN Training on HPC Systems

Arpan Jain

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Tags: Artificial intelligence, Computer science, CUDA, Data parallelism, Deep learning, HPC, Machine learning, MPI, Neural networks, nVidia, Speech recognition, Tesla K80, Tesla V100, Thesis

October 1, 2023 by hgpu

Compressed Real Numbers for AI: a case-study using a RISC-V CPU

Federico Rossi, Marco Cococcioni, Roger Ferrer Ibàñez, Jesùs Labarta, Filippo Mantovani, Marc Casas, Emanuele Ruffaldi, Sergio Saponara

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Tags: AI, Compression, Computer science, Machine learning, Neural networks

September 24, 2023 by hgpu

Leveraging Memory Copy Overlap for Efficient Sparse Matrix-Vector Multiplication on GPUs

Guangsen Zeng, Yi Zou

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia A40, Sparse matrix

September 6, 2023 by hgpu

Increased reliability on Intel GPUs via software diverse redundancy

Nikolaos Andriotis

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Tags: Computer science, Deep learning, Machine learning, MPI, nVidia, OpenCL, Thesis

August 20, 2023 by hgpu

SYnergy: Fine-grained Energy-Efficient Heterogeneous Computing for Scalable Energy Saving

Kaijie Fan, Marco D'Antonio, Lorenzo Carpentieri, Biagio Cosenza, Federico Ficarelli, Daniele Cesarini

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, Energy-efficient computing, GPU cluster, Heterogeneous systems, Machine learning, nVidia, nVidia V100, SYCL

August 13, 2023 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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