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Orion: Interference-aware, Fine-grained GPU Sharing for ML Applications

Foteini Strati, Xianzhe Ma, Ana Klimovic

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Tags: Computer science, CUDA, Deep learning, Machine learning, Neural networks, nVidia, nVidia A100, nVidia V100, Package, PyTorch

January 14, 2024 by hgpu

Deep Learning for Obfuscated Code Analysis

Alexander Shroyer

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce RTX 2060, OpenCL, Package, PyTorch, Thesis

January 7, 2024 by hgpu

UniFL: Accelerating Federated Learning Using Heterogeneous Hardware Under a Unified Framework

Biyao Che, Zixiao Wang, Ying Chen, Liang Guo, Yuan Liu, Yuan Tian, Jizhuang Zhao

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Tags: Computer science, CUDA, Deep learning, FPGA, nVidia, OpenCL, Security, Tesla T4

January 7, 2024 by hgpu

Compiler-centric across-stack deep learning acceleration

Perry Gibson

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Tags: Compilers, Computer science, Deep learning, Design space exploration, nVidia, nVidia Jetson AGX Xavier, nVidia Titan RTX, Thesis

December 10, 2023 by hgpu

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

Fabrizio Ferrandi, Serena Curzel, Leandro Fiorin, Daniele Ielmini, Cristina Silvano, Francesco Conti, Alessio Burrello, Francesco Barchi, Luca Benini, Luciano Lavagno, Teodoro Urso, Enrico Calore, Sebastiano Fabio Schifano, Cristian Zambelli, Maurizio Palesi, Giuseppe Ascia, Enrico Russo, Nicola Petra, Davide De Caro, Gennaro Di Meo, Valeria Cardellini, Salvatore Filippone, Francesco Lo Presti, Francesco Silvestri, Paolo Palazzari, Stefania Perri

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Tags: AI, Artificial intelligence, Computer science, CUDA, Deep learning, Design space exploration, Hardware Architecture, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia H100, OpenCL, survey

December 3, 2023 by hgpu

Frameworks in Medical Image Analysis with Deep Neural Networks

Dominik Müller

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Tags: Computed tomography, CT, Deep learning, Image processing, Medicine, Microscopy, Neural networks, nVidia, nVidia Titan RTX, Package, Python, Thesis, Tomography

November 27, 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

Redco: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs

Bowen Tan, Yun Zhu, Lijuan Liu, Hongyi Wang, Yonghao Zhuang, Jindong Chen, Eric Xing, Zhiting Hu

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Tags: Computer science, Deep learning, Distributed computing, nVidia, nVidia A100, Package, TPU

November 5, 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

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

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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