Real-world engineering problems commonly have multiple objectives that have to be tuned simultaneously. The trade-off Pareto front resulting from the tuning is used as a decision-making tool for selecting the best trade-off for any specific user scenario. Specifically, multi-objective optimization is a crucial matter in programming languages, compilers and hardware design space exploration (DSE) because real-world applications often rely on a trade-off between several objectives such as throughput, latency, memory usage, energy, area, etc.
While the growing demand for sophisticated DSE methods has triggered the development of a wide range of approaches and frameworks, none to date are featured enough to fully address the complexities of DSE in the PL/compilers domain. To address this problem, we introduce a new methodology and a framework dubbed HyperMapper. HyperMapper is a machine learning-based tool designed for the computer systems community and can handle design spaces consisting of multiple objectives and numerical/discrete variables. Emphasis is on exploiting user prior knowledge via modeling of the design space parameters distributions. Given the years of hand-tuning experience in optimizing hardware, designers bear a high level of confidence. HyperMapper gives means to inject knowledge in the search algorithm. The framework uses a Bayesian Optimization algorithm, i.e., construct and utilize a surrogate model of the latent function to guide the search process. A key advantage of having a model is the reduction of the optimization time budget. HyperMapper is a plug-and-play framework that makes it easy for compiler/hardware designers to explore their search spaces.
To aid the comparison of HyperMapper with other DSE tools, we provide a taxonomy of existing tools.
DSE-2020 will be virtual, like PLDI. See the main PLDI page for news and updates.
Total duration: 3 hours. Time is in PDT.
|8:00||Introduction||Luigi Nardi||10 min||link|
|8:10||Design Space Exploration||Luigi Nardi||30 min||link|
|8:40||Hands-on: HyperMapper Demo||Artur Souza||25 min||link|
|9:15||The Spatial programming language and compiler||Matt Feldman||30 min||link|
|9:45||Hands-on: Spatial Demo||Artur Souza & Matt Feldman||30 min||Matt / Artur|
|10:25||DSE advanced topics||Artur Souza||30 min||link|
|10:55||Discussions/panel - Q&A||All speakers||Flexible|
- Practical Design Space Exploration: paper
- Spatial: A language and compiler for application accelerators: paper
Luigi Nardi: luigi.nardi at cs.lth.se
Mon 15 JunDisplayed time zone: Pacific Time (US & Canada) change
08:00 - 12:00
Tutorial: Design Space ExplorationTutorials at Design Space Exploration tutorial live stream
|Design Space Exploration|
Matthew Feldman Stanford University, USA, Artur Souza Universidade Federal de Minas Gerais (UFMG), Luigi Nardi Lund University and Stanford University, Kunle Olukotun Stanford University