Electrical and Information Technology

Faculty of Engineering, LTH

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PLDI 2020 Tutorial: Design Space Exploration - also broadcasted on Youtube


From: 2020-06-15 17:00 to 21:00
Place: Online at the Zoom platform (Link by registration) and Youtube
Contact: luigi [dot] nardi [at] cs [dot] lth [dot] se
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When: 15 June at 17.00 to 21.00 (8.00-12.00 PDT)

The registration is closed. Due to  +1000 registrations the event will also be broadcasted at Youtube:

Participants can ask questions on the Slack channel at the PLDI2020 Slack


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.

Programme details at:

Contact; Luigi Nardi WASP-AI assistant professor, Computer Science, Lund University. Follow:  

Logo for Programming Language Design and Implementation (PLDI) Online conference 15-20 June, 2020To participate is free of charge. 

The tutorial is part of the Programming Language Design and Implementation (PLDI) Online conference 15-20 June