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PLDI 2020
Mon 15 - Fri 19 June 2020


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.

COVID-19 Update

DSE-2020 will be virtual, like PLDI. See the main PLDI page for news and updates.

Event Video



Total duration: 3 hours. Time is in PDT.

Time Topic Speaker Duration Slides
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
9:05 Break 10 min
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
10:15 Break 10 min
10:25 DSE advanced topics Artur Souza 30 min link
10:55 Discussions/panel - Q&A All speakers Flexible

Hands-on Material

  1. HyperMapper framework: site
  2. Spatial language: site


  1. Practical Design Space Exploration: paper
  2. Spatial: A language and compiler for application accelerators: paper


Luigi Nardi: luigi.nardi at cs.lth.se

Twitter: #DSEPLDI

Mon 15 Jun

Displayed 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