DAE4HLS enables explicit decoupling of access and execute in HLS to unlock memory-level parallelism, delivering 10-79x speedups for complex workloads on commercial and dynamic HLS tools.
Araya-Polo, J
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An adaptive anisotropic composite quadrature strategy combined with refresh-based training narrows the gap between training and reference losses in neural residual minimization for PDEs while using quadrature points more efficiently.
COMPASS formalizes HPC configuration questions as ML tasks on traces, quantifies recommendation trustworthiness, and delivers 65.93% lower average job turnaround time plus 80.93% lower node usage versus prior methods in simulator tests.
A method for adjoint differentiation of stencil loops that preserves their structure and parallelizability via combined AD and loop transformations, released as the PerforAD tool with seismic and CFD test cases.
SET is a new CUDA runtime framework that combines event-chaining, work-stealing, and per-stream buffers in graph-based pipelines to deliver 1.15-1.44X speedups and 18-54% lower scheduling overhead versus prior CUDA graph methods.
The thesis introduces a topology-aware tensor-network heuristic called SpinGlassPEPS.jl and thermodynamic metrics to benchmark quantum annealers on Ising problems while accounting for dissipation and effective temperature.
LTQ_n admits floor(n/2) completely edge-independent spanning trees via a constructive algorithm.
A systematic mapping study of Karma mechanisms that compares applications, structures design parameters, and maps future research directions in non-monetary resource allocation.
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Adaptive anisotropic composite quadratures for residual minimisation in neural PDE approximations
An adaptive anisotropic composite quadrature strategy combined with refresh-based training narrows the gap between training and reference losses in neural residual minimization for PDEs while using quadrature points more efficiently.