Design Space and Application Autotuning for Runtime Adaptivity in Multicore Architectures

Friday, June 03, 2016
10:30 AM to 11:30 AM
POB 2.402
Free and open to the public

Given the increasing complexity of manycore architectures, a wide range of architecture parameters must be tuned at design-time to find the best tradeoffs in terms of multiple metrics such as energy and delay. Given the huge design space of manycore architectures, automatic design space exploration is necessary to systematically support at design-time the exploration and the comparison of the design alternatives in terms of multiple competing objectives. At runtime, manycore architectures offer a set of resources that can be assigned and managed dynamically to get a specified Quality of Service. Applications can expose to the runtime a set of software knobs (including application parameters, code transformations and code variants) to trade-off Quality of Results and Throughput. Resource management and application autotuning are key issues for enabling computing systems to operate close to optimal efficiency by adjusting their behavior in the face of changing conditions, operating environments, usage contexts and resource availability while meeting the requirements on energy-efficiency and Quality-of-Service.

This talk will present multi-objective DSE techniques for many-core architectures. The key techniques include a set of sampling and optimization techniques for finding Pareto points and Design of Experiment techniques to identify the experimentation plan. Machine learning techniques can be used to obtain a prediction of the system behavior based on the set of training data generated by DoE. This talk also presents an application autotuning framework to tune the software knobs in an adaptive multi-application scenario. To support this scenario, where different applications are running concurrently on the same platform, the system resources should be assigned and managed efficiently to the active applications. The approach exploits the concept of orthogonality between application autotuning and runtime management of system resources to support multiple adaptive applications. Overall, the main challenge is to exploit design-time and run-time concepts to lead to an effective way of “self-aware” computing.

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Cristina Silvano

Cristina Silvano

Associate Professor
Politecnico di Milano

Cristina Silvano is an Associate Professor (with tenure) of Computer Engineering at the Politecnico di Milano. She received her MS degree (Laurea) in Electrical Engineering from Politecnico di Milano in 1987. From 1987 to 1996, she was Senior Design Engineer at the R&D Labs of Group Bull in Pregnana Milanese (Italy) and Visiting Engineer at Bull R&D Labs in Billerica (US) (1988-89) and at IBM Somerset Design Center, Austin (US) (1993-1994). She received her Ph.D. in Computer Engineering from the University of Brescia in 1999. 

She was Assistant Professor of Computer Science at the University of Milano (2000 -2002) and then Associate Professor at the Politecnico di Milano (2002-present). Her primary research interests focus on computer architectures and electronic design automation, with particular emphasis on power-aware design for embedded systems, design space exploration and runtime resource management for manycore architectures. Her research has been funded by several national and international projects. In particular, she was Principal Investigator of some industrial funded research projects in collaboration with STMicroelectronics.

She is currently Project Coordinator for the H2020-FET-HPC ANTAREX European project on autotuning and adaptivity for energy-efficient Exascale High Performance Computing systems. She has published more than 140 papers in premier international journals and conferences. She was co-editor of two scientific books edited by Springer in 2010 and 2011.