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Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores

Shaobo Ma, Chao Fang, Haikuo Shao, Zhongfeng Wang

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Tags: Computer science, CUDA, Data recovery, Extended precision, LLM, Matrix multiplication, nVidia, nVidia GeForce RTX 3090, Security

October 6, 2024 by hgpu

Event-Based OpenMP Tasks for Time-Sensitive GPU-Accelerated Systems

Cyril Cetre, Chenle Yu, Sara Royuela, Rémi Barrere, Eduardo Quiñones, Damien Gratadour

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Tags: AMD Radeon Instinct MI100, ATI, Computer science, CUDA, HIP, HPC, nVidia, nVidia A100, Performance

October 6, 2024 by hgpu

Benchmarking Thread Block Cluster

Tim Lühnen, Tobias Marschner, Sohan Lal

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia H100, PTX

October 6, 2024 by hgpu

HPC acceleration of large (min, +) matrix products to compute domination-type parameters in graphs

E.M. Garzón, J.A. Martínez, J.J. Moreno, M.L. Puertas

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Tags: Algorithms, Computer science, CUDA, Graph theory, HPC, nVidia, OpenMP, Package, Tesla V100

September 29, 2024 by hgpu

Automatic Generation of OpenCL Code through Polyhedral Compilation with LLM

Marek Palkowski, Mateusz Gruzewski

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Tags: AMD Radeon RX 6800, ATI, Code generation, Computer science, CUDA, LLM, nVidia, nVidia A100, OpenCL, OpenMP

September 29, 2024 by hgpu

Bitstream Database-Driven FPGA Programming Flow Based on Standard OpenCL

Topi Leppänen, Leevi Leppänen, Joonas Multanen, Pekka Jääskeläinen

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Tags: Computer science, Databases, FPGA, Heterogeneous systems, OpenCL, Package

September 29, 2024 by hgpu

Challenging Portability Paradigms: FPGA Acceleration Using SYCL and OpenCL

Manuel de Castro, Francisco J. andújar, Roberto R. Osorio, Rocío Carratalá-Sáez, Diego R. Llanos

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Tags: Computer science, FPGA, OpenCL, Optimization, performance portability, SYCL

September 22, 2024 by hgpu

Collection skeletons: declarative abstractions for data collections

Zhibo Li

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Tags: Computer science, Data Structures and Algorithms, OpenCL, performance portability, SYCL, Thesis

September 22, 2024 by hgpu

A Study of Performance Programming of CPU, GPU accelerated Computers and SIMD Architecture

Xinyao Yi

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Tags: Computer science, CUDA, Heterogeneous systems, HPC, nVidia, nVidia A100, OpenMP, Performance, Programming techniques

September 22, 2024 by hgpu

DNA sequence alignment: An assignment for OpenMP, MPI, and CUDA/OpenCL

Arturo Gonzalez-Escribano, Diego García-Álvarez, Jesús Cámara

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Tags: Bioinformatics, Biology, CUDA, MPI, nVidia, nVidia A100, nVidia GeForce GTX Titan X, nVidia GeForce RTX 4050, OpenCL, Optimization, Sequence alignment

September 15, 2024 by hgpu

Enhancing Code Portability, Problem Scale, and Storage Efficiency in Exascale Applicationsin Exascale Applications

Nigel Tan

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Tags: AMD Radeon Vega VII, ATI, Computer science, CUDA, Heterogeneous systems, HPC, nVidia, nVidia A100, nVidia DGX-A100, nVidia Quadro RTX 5000, OpenMP, OpenMPI, Particle-in-cell methods, performance portability, Tesla V100, Thesis

September 15, 2024 by hgpu

Refining HPCToolkit for application performance analysis at exascale

Laksono Adhianto, Jonathon Anderson, Robert Matthew Barnett, Dragana Grbic, Vladimir Indic, Mark Krentel, Yumeng Liu, Srdan Milakovíc, Wileam Phan, John Mellor-Crumme

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Tags: AMD Radeon Instinct MI300A, ATI, Computer science, CUDA, HPC, MPI, OpenCL, Package, Performance

September 15, 2024 by hgpu

OpScanner

A Study of Floating-Point Precision Tuning in Deep Learning Operators Implementations

Atlas CLI: Machine Learning (ML) Lifecycle & Transparency Manager

Scalable GPU-Based Integrity Verification for Large Machine Learning Models

transformers_tvm: Implementation of Encoder Decoder transformer on TVM

Enhancing Transformer Performance and Portability through Auto-tuning Frameworks

INT v.s. FP: A framework to compare low-bit integer and float-point formats

INT v.s. FP: A Comprehensive Study of Fine-Grained Low-bit Quantization Formats

AutoDock-GPU: AutoDock for GPUs and other accelerators

Architecting Tensor Core-Based Reductions for Irregular Molecular Docking Kernels

NCCLX: collective communication framework

Collective Communication for 100k+ GPUs

Tutoring LLM into a Better CUDA Optimizer

Tutoring LLM into a Better CUDA Optimizer

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Adaptivity in AdaptiveCpp: Optimizing Performance by Leveraging Runtime Information During JIT-Compilation

Kernel Library for LLM Serving

Compiler and Runtime Systems for Generative AI Models

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

Neptune: Advanced ML Operator Fusion for Locality and Parallelism on GPUs

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