Wed 17 Jun 2015 09:40 - 10:05 at PLDI Main BLUE (Portland 254-255) - Performance Chair(s): Mary Hall

A daunting challenge faced by program performance autotuning is input sensitivity, where the best autotuned configuration may vary with different input sets. This paper presents a novel two-level input learning algorithm to tackle the challenge for an important class of autotuning problems, algorithmic autotuning. The new approach uses a two-level input clustering method to automatically refine input grouping, feature selection, and classifier construction. Its design solves a series of open issues that are particularly essential to algorithmic autotuning, including the enormous optimization space, complex influence by deep input features, high cost in feature extraction, and variable accuracy of algorithmic choices. Experimental results show that the new solution yields up to a 3x speedup over using a single configuration for all inputs, and a 34x speedup over a traditional one-level method for addressing input sensitivity in program optimizations.

Wed 17 Jun

Displayed time zone: Tijuana, Baja California change

09:15 - 10:55
PerformanceResearch Papers at PLDI Main BLUE (Portland 254-255)
Chair(s): Mary Hall University of Utah
09:15
25m
Talk
Automated Detection of Performance Bugs via Static Analysis
Research Papers
Oswaldo Olivo , Isil Dillig University of Texas, Austin, Calvin Lin UT Austin
Media Attached
09:40
25m
Talk
Autotuning Algorithmic Choice for Input Sensitivity
Research Papers
Yufei Ding North Carolina State University, Jason Ansel Massachusetts Institute of Technology, Kalyan Veeramachaneni Massachusetts Institute of Technology, Xipeng Shen North Carolina State University, Una-May O’Reilly Massachusetts Institute of Technology, Saman Amarasinghe MIT
Link to publication Media Attached
10:05
25m
Talk
Helium: Lifting High-Performance Stencil Kernels from Stripped x86 Binaries to Halide DSL Code
Research Papers
Charith Mendis MIT CSAIL, Jeffrey Bosboom MIT CSAIL, Kevin Wu MIT CSAIL, Shoaib Kamil MIT CSAIL, USA, Jonathan Ragan-Kelley Stanford, Sylvain Paris Adobe, Qin Zhao Google, Saman Amarasinghe MIT
Media Attached
10:30
25m
Talk
Profile-Guided Meta-Programming
Research Papers
William J. Bowman Northeastern University, Swaha Miller Cisco Systems, Inc, Vincent St-Amour Northeastern University, R. Kent Dybvig Cisco Systems, Inc
Link to publication Media Attached