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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,786 papers · 148 categories

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3 results for “Compiler-level”
Compiler-Level Matrix Multiplication Optimization for Deep Learningcs.LG

Paper optimizes GEMM for deep learning models, improving performance.

problem Limited GEMM optimization in deep learning frameworks restricts performance on different hardware.
method Proposes two novel algorithms: Greedy Best First Search (G-BFS) and Neighborhood Actor Advantage Critic (N-A2C) based on TVM framework.
result Significant performance improvements in GEMM computation time, achieving up to 40% savings.
GEMM optimizationDeep learningCompiler-levelReinforcement learningGreedy search
2019-09-23abs ↗pdf ↗
Memory Safe Computations with XLA Compilercs.LG

XLA compiler extension improves memory efficiency for machine learning.

problem Memory constraints limit the scalability of memory-intensive machine learning algorithms.
method Developed an XLA compiler extension that adjusts algorithm data-flow representation to fit memory limits.
result k-nearest neighbour and sparse Gaussian process regression can be run at larger scales.
machine learningmemory efficiencyXLA compilerlinear algebraneural networks
2022-06-28abs ↗pdf ↗