New methods improve autotuning of exascale applications by 1.5x.
problem Finding optimal performance parameters for exascale applications.
method Multitask and transfer learning for autotuning.
result Average 1.5x improvement in application runtime.
On-device research index
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.
169,051 papers · 148 categories
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6 results for “exascale”
A new method for faster spatial modeling on exascale computers.
problem Scalable, memory-efficient machine learning for spatially distributed data.
method Partitioned Sparse Variational Gaussian Process (PSVGP) with decentralized communication.
result Improved spatial predictions and better model fit with minimal overhead.
Efficient deep learning on exascale supercomputers solves materials imaging inverse problems.
problem Solving scientific inverse problems in materials imaging using deep learning.
method Novel communication strategies in synchronous distributed deep learning, including decentralized gradient reduction and computational graph-aware grouping.
result Achieved near-linear scaling of distributed training up to 27,600 GPUs on Summit, reaching 2.15(4) EFLOPS.
New algorithm learns from distributed, heterogeneous data without shuffling.
problem Efficiently learning from distributed, heterogeneous data in exascale simulations.
method Block-random gradient descent algorithm for distributed, heterogeneous data.
result Algorithm enables in situ learning without pre-shuffling data.
New neural network accelerates cancer research analysis.
problem Slow inference speeds limit cancer research analysis.
method Automatically generated, tailored neural network architecture.
result 16x faster inference on cancer pathology data.
A new algorithm avoids worst-case outcomes in risky contexts.
problem Risk-averse behavior in contextual bandits is challenging.
method Developed a first risk-averse contextual bandit algorithm with online regret guarantees.
result First algorithm with an online regret guarantee for risk-averse contextual bandits.