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Found 10 papers, 7 papers with code
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs
TpuGraphs provides 25x more graphs than the largest graph property prediction dataset (with comparable graph sizes), and 770x larger graphs on average compared to existing performance prediction datasets on machine learning programs.
FLuRKA: Fast fused Low-Rank & Kernel Attention
FLuRKA provide sizable performance gains over these approximate techniques and are of high quality.
SENSEi: Input-Sensitive Compilation for Accelerating GNNs
We leverage this observation to propose SENSEi, a system that exposes different sparse and dense matrix primitive compositions based on different matrix re-associations of GNN computations and selects the best among them based on input attributes.
Learning Large Graph Property Prediction via Graph Segment Training
Here we propose Graph Segment Training (GST), a general framework that utilizes a divide-and-conquer approach to allow learning large graph property prediction with a constant memory footprint.
COMET: Neural Cost Model Explanation Framework
We generate and compare COMET's explanations for the popular neural cost model, Ithemal against those for an accurate CPU simulation-based cost model, uiCA.
GRANITE: A Graph Neural Network Model for Basic Block Throughput Estimation
In this paper, we introduce GRANITE, a new machine learning model that estimates the throughput of basic blocks across different microarchitectures.
DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates
In this paper we present DiffTune, a system for learning the parameters of x86 basic block CPU simulators from coarse-grained end-to-end measurements.
A Learned Performance Model for Tensor Processing Units
Accurate hardware performance models are critical to efficient code generation.
Compiler Auto-Vectorization with Imitation Learning
We show that the learnt policy produces a vectorization scheme which is better than industry standard compiler heuristics both in terms of static measures and runtime performance.
Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks
Predicting the number of clock cycles a processor takes to execute a block of assembly instructions in steady state (the throughput) is important for both compiler designers and performance engineers.
