arXiv Machine Learning
GPUMAP: GPU-Accelerated Progressive Uniform Manifold Approximation and Projection
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GPU-Accelerated ANNS: Quantized for Speed, Built for Change
arXiv:2601. 07048v5 Announce Type: replace-cross Abstract: Approximate nearest neighbor search (ANNS) is a core problem in machine learning and information retrieval applications.
arXiv Machine Learning
Jul 10
CTA-Pipelining: A Latency-Oriented Spatial Scaling Method for Multi-GPU Systems
arXiv:2607. 07862v1 Announce Type: cross Abstract: The evolution of compute infrastructure has transformed multi-GPU systems into tightly integrated shared-memory structures.
arXiv AI
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Local Inference of LLMs on Long Contexts: A Data Systems Perspective
arXiv:2610.28223v1 Announce Type: cross Abstract: Large language models can solve complex problems across a variety of subjects, such as mathematics and coding. For such complex analytical tasks, mod...
arXiv Machine Learning
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SparseDitto: An Agentic Sparse Compilation Framework through Architecture-Aware Synthesis on GPUs
SparseDitto is an agentic sparse compilation framework that jointly synthesizes representation, execution schedule, and hardware mapping for sparse matrix computations on GPUs. It uses structural analysis, a learned template-ranking prior, and LLM-guided lowering to generate CUDA code, with target-GPU profiling refining the plan. The framework supports multiple operators such as SpMV, SpMM, and SpGEMM, adapts to different hardware, and achieves significant speedups over cuSPARSE, including up to 146.61× on certain matrices and 3.39× acceleration for full-batch GCN training.
arXiv Machine Learning
Aug 6
SparseDitto: Customizing GPU Kernels for Different Sparsity Patterns with LLM-Based Agentic System
arXiv:2608. 05033v1 Announce Type: cross Abstract: Sparse matrix kernels are fundamental to scientific computing, graph analytics, and machine learning.
arXiv Machine Learning
Jul 10
Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation
arXiv:2604. 10180v2 Announce Type: replace-cross Abstract: Disaggregation maps parts of an AI workload to different types of GPUs, offering a path to utilize modern heterogeneous GPU clusters.