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Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

Urvij Saroliya, Eishi Arima, Dai Liu, Martin Schulz

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Tags: Computer science, CUDA, Heterogeneous systems, Machine learning, nVidia, nVidia A100, PC cluster, Task scheduling

May 20, 2024 by hgpu

Workload Scheduling on Heterogeneous Devices

Harsh Khetawat, Frank Mueller

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Tags: Computer science, FPGA, Heterogeneous systems, nVidia, nVidia GeForce RTX 2060, OpenCL, Task scheduling

May 20, 2024 by hgpu

CuPBoP: Making CUDA a Portable Language

Ruobing Han, Jun Chen, Bhanu Garg, Xule Zhou, John Lu, Jeffrey Young, Jaewoong Sim, Hyesoon Kim

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Tags: Computer science, CUDA, HIP, nVidia, nVidia A30, OpenCL, Package, Portability, SYCL

May 12, 2024 by hgpu

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

Ruixin Wang, Minghai Lu, Cody Hao Yu, Yi-Hsiang Lai, Tianyi Zhang

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Tags: Computer science, CUDA, Deep learning, DSL, nVidia, Package, Performance, PyTorch, Tesla T4

May 12, 2024 by hgpu

Deep Learning Inference on Heterogeneous Mobile Processors: Potentials and Pitfalls

Sicong Liu, Wentao Zhou, Zimu Zhou, Bin Guo, Minfan Wang, Cheng Fang, Zheng Lin, Zhiwen Yu

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Tags: Computer science, Deep learning, Heterogeneous systems

May 12, 2024 by hgpu

Optimizing Hardware Resource Partitioning and Job Allocations on Modern GPUs under Power Caps

Eishi Arima, Minjoon Kang, Issa Saba, Josef Weidendorfer, Carsten Trinitis, Martin Schulz

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Tags: Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, nVidia, nVidia A100, Performance

May 12, 2024 by hgpu

A Survey of Deep Learning Library Testing Methods

Xiaoyu Zhang, Weipeng Jiang, Chao Shen, Qi Li, Qian Wang, Chenhao Lin, Xiaohong Guan

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Tags: Computer science, Deep learning, Security, survey

May 5, 2024 by hgpu

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Andrey Alekseenko, Szilárd Páll, Erik Lindahl

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Tags: AMD Radeon Instinct MI250X, ATI, Biomolecules, Computational Physics, Computer science, Heterogeneous systems, Molecular dynamics, Molecular simulation, Performance, SYCL

May 5, 2024 by hgpu

Porting HPC Applications to AMD Instinct MI300A Using Unified Memory and OpenMP

Suyash Tandon, Leopold Grinberg, Gheorghe-Teodor Bercea, Carlo Bertolli, Mark Olesen, Simone Bnà, Nicholas Malaya

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Tags: AMD Radeon Instinct MI210, AMD Radeon Instinct MI300A, ATI, cfd, Computer science, Fluid dynamics, HPC, nVidia, nVidia A100, nVidia H100, OpenMP

May 5, 2024 by hgpu

Experiences with implementing Kokkos’ SYCL backend

Daniel Arndt, Damien Lebrun-Grandie, Christian Trott

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Tags: Benchmarking, Computer science, HPC, Intel, Intel Data Center GPU Max 1550, Package, performance portability

May 5, 2024 by hgpu

Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs

Endri Taka, Dimitrios Gourounas, Andreas Gerstlauer, Diana Marculescu, Aman Arora

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Tags: AI, Computer science, Deep learning, FPGA, GEMM, Matrix multiplication

April 21, 2024 by hgpu

Software Optimization and Orchestration for Heterogeneous and Distributed Architectures

Francesco Lumpp

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia Jetson AGX Xavier, nVidia Jetson TX2, Performance, Thesis

April 21, 2024 by hgpu

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Automating Heterogeneous Parallelism in Numerical Differential Equations

Dat3M: Memory Model Aware Verification

Towards Unified Analysis of GPU Consistency

NVIDIA Federated Learning Application Runtime Environment

Supercharging Federated Learning with Flower and NVIDIA FLARE

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

CATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization

General-purpose Polyhedral Compilers

A Survey of General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Stencil Computations on AMD and Nvidia Graphics Processors: Performance and Tuning Strategies

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs

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high performance computing on graphics processing units: hgpu.org

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

Workload Scheduling on Heterogeneous Devices

CuPBoP: Making CUDA a Portable Language

Automated Deep Learning Optimization via DSL-Based Source Code Transformation

Deep Learning Inference on Heterogeneous Mobile Processors: Potentials and Pitfalls

Optimizing Hardware Resource Partitioning and Job Allocations on Modern GPUs under Power Caps

A Survey of Deep Learning Library Testing Methods

GROMACS on AMD GPU-Based HPC Platforms: Using SYCL for Performance and Portability

Porting HPC Applications to AMD Instinct MI300A Using Unified Memory and OpenMP

Experiences with implementing Kokkos’ SYCL backend

Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs

Software Optimization and Orchestration for Heterogeneous and Distributed Architectures

Recent source codes

GPUODEBenchmarks: Comparsion of Julia's GPU Kernel based ODE solvers with other open-source GPU ODE solvers

Dat3M: Memory Model Aware Verification

NVIDIA Federated Learning Application Runtime Environment

Chat AI: A Seamless Slurm-Native Solution for HPC-Based Services

PSCToolkit: solving sparse linear systems with a large number of GPUs

CATBench: Benchmarking Framework

General-purpose Polyhedral Compilers

Optimal Kernel Orchestration for Tensor Programs with Korch

Astaroth: A Scalable Multi-GPU Library for Stencil Computations

Bag of Tricks: Benchmarking of Jailbreak Attacks on LLMs. Empirical tricks for LLM Jailbreaking

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