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

Wei Da, Evangelia Kalyvianaki

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Tags: Artificial intelligence, Computer science, CUDA, LLM, nVidia, nVidia A30, Package, Task scheduling

August 17, 2025 by hgpu

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

Pre-Training LLMs on a budget: A comparison of three optimizers

Joel Schlotthauer, Christian Kroos, Chris Hinze, Viktor Hangya, Luzian Hahn, Fabian Küch

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Tags: Artificial intelligence, Computer science, CUDA, LLM, Machine learning, nVidia, nVidia A100

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

CUDA-LLM: LLMs Can Write Efficient CUDA Kernels

Wentao Chen, Jiace Zhu, Qi Fan, Yehan Ma, An Zou

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Tags: Artificial intelligence, Code generation, Computer sceince, CUDA, LLM, nVidia, nVidia GeForce GTX 1660, nVidia GeForce RTX 3090 Ti

June 15, 2025 by hgpu

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning

Yong-Cheng Liaw, Shuo-Han Chen

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Tags: Artificial intelligence, Benchmarking, Computer science, LLM, Memory, nVidia, nVidia H100, nVidia RTX A5000

June 8, 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

Efficient deep learning inference on end devices

Ehsan Aghapour

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Tags: Artificial intelligence, Computer science, Deep learning, Heterogeneous systems, OpenCL, Package, Thesis

May 4, 2025 by hgpu

Towards autonomous resource management: Deep learning prediction of CPU-GPU load balancing

Inigo Gabirondo Lopez

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Tags: AMD Radeon HD 7970, Artificial intelligence, ATI, Computer science, Deep learning, Heterogeneous systems, load balancing, nVidia, nVidia GeForce GTX 970, OpenCL

February 10, 2025 by hgpu

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

Huanting Wang, Patrick Lenihan, Zheng Wang

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Tags: Artificial intelligence, Computer science, Machine learning, OpenCL, Optimization, Package, Software Engineering

January 6, 2025 by hgpu

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

Avinash Maurya

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Tags: AI, Artificial intelligence, Computer science, CUDA, Deep learning, HPC, nVidia, nVidia DGX-A100, Package, Thesis

December 29, 2024 by hgpu

SoK: A Systems Perspective on Compound AI Threats and Countermeasures

Sarbartha Banerjee, Prateek Sahu, Mulong Luo, Anjo Vahldiek-Oberwagner, Neeraja J. Yadwadkar, Mohit Tiwari

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Tags: AI, Artificial intelligence, Computer science, LLM, Machine learning, Security

November 24, 2024 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

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

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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high performance computing on graphics processing units: hgpu.org

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

Kevin: Multi-Turn RL for Generating CUDA Kernels

Pre-Training LLMs on a budget: A comparison of three optimizers

Omniwise: Predicting GPU Kernels Performance with LLMs

CUDA-LLM: LLMs Can Write Efficient CUDA Kernels

MemAscend: System Memory Optimization for SSD-Offloaded LLM Fine-Tuning

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

Efficient deep learning inference on end devices

Towards autonomous resource management: Deep learning prediction of CPU-GPU load balancing

Enhancing Deployment-Time Predictive Model Robustness for Code Analysis and Optimization

Scalable Access-Pattern Aware I/O Acceleration and Multi-Tiered Data Management for HPC and AI Workloads

SoK: A Systems Perspective on Compound AI Threats and Countermeasures

Recent source codes

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

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

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

Bandicoot: C++ library for GPU accelerated linear algebra

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

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

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

SIGMo: Scalable Isomorphism Graph Matching on GPUs

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

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