high performance computing on graphics processing units: hgpu.org

hgpu.org » Task scheduling

ML Inference Scheduling with Predictable Latency

Haidong Zhao, Nikolaos Georgantas

View

Download (PDF)

Tags: Computer science, Machine learning, nVidia, nVidia L4, Task scheduling

December 21, 2025 by hgpu

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Shruti Dongare, Redwan Ibne Seraj Khan, Hadeel Albahar, Nannan Zhao, Diego Melendez Maita, Ali R. Butt

View

Download (PDF)

Source codes

Tags: Computer science, Deep learning, GPU cluster, nVidia, nVidia H100, Package, Task scheduling

December 15, 2025 by hgpu

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

Wei Da, Evangelia Kalyvianaki

View

Download (PDF)

Source codes

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

August 17, 2025 by hgpu

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

Paul Cardosi, Bérenger Bramas

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, Heterogeneous systems, HIP, nVidia, nVidia A100, nVidia P100, nVidia Quadro RTX 8000, nVidia V100, Package, Task scheduling

July 20, 2025 by hgpu

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling

Guilin Zhang, Wulan Guo, Ziqi Tan, Qiang Guan, Hailong Jiang

View

Download (PDF)

Source codes

Tags: Computer science, nVidia, nVidia GeForce RTX 3080, Package, Task scheduling

July 13, 2025 by hgpu

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

Joshua Bakita, James H. Anderson

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, nVidia, nVidia GeForce RTX 4090, nVidia RTX 6000 Ada, Package, Task scheduling

July 13, 2025 by hgpu

Concurrent Scheduling of High-Level Parallel Programs on Multi-GPU Systems

Fabian Knorr, Philip Salzmann, Peter Thoman, Thomas Fahringer

View

Download (PDF)

Source codes

Tags: Benchmarking, Computer science, HPC, nVidia, nVidia A100, Package, performance portability, SYCL, Task scheduling

March 23, 2025 by hgpu

Scheduling Languages: A Past, Present, and Future Taxonomy

Mary Hall, Cosmin Oancea, Anne C. Elster, Ari Rasch, Sameeran Joshi, Amir Mohammad Tavakkoli, Richard Schulze

View

Download (PDF)

Tags: Auto-Tuning, Compilers, Computer science, Performance, Programming Languages, Task scheduling

November 3, 2024 by hgpu

Is the GPU Half-Empty or Half-Full? Practical Scheduling Techniques for LLMs

Ferdi Kossmann, Bruce Fontaine, Daya Khudia, Michael Cafarella, Samuel Madden

View

Download (PDF)

Tags: Computer science, LLM, NLP, nVidia, nVidia A100, nVidia H100, Task scheduling

November 3, 2024 by hgpu

Efficient Configuration of Heterogeneous Resources and Task Scheduling Strategies in Deep Learning Auto-Tuning Systems

Pao-Yi Ken, Chao-Chin Wu

View

Download (PDF)

Tags: AI, Artificial intelligence, Computer science, Deep learning, Machine learning, nVidia, nVidia RTX A6000, Task scheduling

October 20, 2024 by hgpu

Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing

Yizhou Luo, Qiang Wang, Shaohuai Shi, Jiaxin Lai, Shuhan Qi, Jiajia Zhang, Xuan Wang

View

Download (PDF)

Tags: Computer science, CUDA, Deep learning, GPU cluster, nVidia, nVidia GeForce RTX 2080 Ti, OpenMPI, PyTorch, Task scheduling

August 4, 2024 by hgpu

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

Urvij Saroliya, Eishi Arima, Dai Liu, Martin Schulz

View

Download (PDF)

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

May 20, 2024 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

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

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

ML Inference Scheduling with Predictable Latency

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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

Specx: a C++ task-based runtime system for heterogeneous distributed architectures

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling

Hardware Compute Partitioning on NVIDIA GPUs for Composable Systems

Concurrent Scheduling of High-Level Parallel Programs on Multi-GPU Systems

Scheduling Languages: A Past, Present, and Future Taxonomy

Is the GPU Half-Empty or Half-Full? Practical Scheduling Techniques for LLMs

Efficient Configuration of Heterogeneous Resources and Task Scheduling Strategies in Deep Learning Auto-Tuning Systems

Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing

Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Most viewed papers (last 30 days)