Optimization
Efficient solutions to large-scale problems.
Much of MERL's research activity involves formulating scientific and engineering problems as optimizations, which can be solved in an efficient way. We have developed fundamental algorithms to better solve classic problems, such as quadratic programs and minimum-cost paths. Our work also involves developing theoretical bounds to understand performance limits.
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Researchers
Daniel
Nikovski
Arvind
Raghunathan
Stefano
Di Cairano
Matthew
Brand
Mouhacine
Benosman
Rien
Quirynen
Scott
Bortoff
Hongbo
Sun
Yebin
Wang
Hiroyuki
Hashimoto
Devesh
Jha
Saleh
Nabi
Christopher
Laughman
Diego
Romeres
Jinyun
Zhang
William
Yerazunis
Daniel
Birch
Claus
Danielson
Toshiaki
Koike-Akino
Keisuke
Kojima
Emil
Laftchiev
David
Millar
Kieran
Parsons
Hongtao
Qiao
Karl
Berntorp
Ankush
Chakrabarty
Abraham
Goldsmith
Jianlin
Guo
Uroš
Kalabić
Kyeong Jin
(K.J.)
Kim
Philip
Orlik
Ye
Wang
Jing
Zhang
Naohiro
Hayashi
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News & Events
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TALK Advances in Accelerated Computing Date & Time: Friday, February 2, 2018; 12:00
Speaker: Dr. David Kaeli, Northeastern University
MERL Host: Abraham Goldsmith
Research Areas: Control, Optimization, Machine Learning, Speech & AudioBrief
GPU computing is alive and well! The GPU has allowed researchers to overcome a number of computational barriers in important problem domains. But still, there remain challenges to use a GPU to target more general purpose applications. GPUs achieve impressive speedups when compared to CPUs, since GPUs have a large number of compute cores and high memory bandwidth. Recent GPU performance is approaching 10 teraflops of single precision performance on a single device. In this talk we will discuss current trends with GPUs, including some advanced features that allow them exploit multi-context grains of parallelism. Further, we consider how GPUs can be treated as cloud-based resources, enabling a GPU-enabled server to deliver HPC cloud services by leveraging virtualization and collaborative filtering. Finally, we argue for for new heterogeneous workloads and discuss the role of the Heterogeneous Systems Architecture (HSA), a standard that further supports integration of the CPU and GPU into a common framework. We present a new class of benchmarks specifically tailored to evaluate the benefits of features supported in the new HSA programming model.
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NEWS MERL invites applications for Visiting Faculty Date: February 15, 2018Brief- University faculty members are invited to spend part or all of their sabbaticals at MERL, pursuing projects of their own choosing in collaboration with MERL researchers.
To apply, a candidate should identify and contact one or more MERL researchers with whom they would like to collaborate. The applicant and a MERL researcher will jointly prepare a proposal that the researcher will champion internally. Please visit the visiting faculty web page for further details: http://www.merl.com/employment/visiting-faculty.php.
The application deadline for positions starting in Summer/Fall 2018 is February 15, 2018.
- University faculty members are invited to spend part or all of their sabbaticals at MERL, pursuing projects of their own choosing in collaboration with MERL researchers.
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Internships
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CA1399: Optimization Algorithms for Stochastic Predictive Control
MERL is looking for a highly motivated individual to work on tailored numerical optimization algorithms and applications of stochastic learning-based model predictive control (MPC) methods. The research will involve the study and development of novel optimization techniques and/or the implementation and validation of algorithms for industrial applications, e.g., related to autonomous driving. The ideal candidate should have experience in either one or multiple of the following topics: stochastic MPC (e.g., scenario trees or tube MPC), convex and non-convex optimization, machine learning, numerical optimization and (inverse) optimal control. PhD students in engineering or mathematics with a focus on stochastic (learning-based) MPC or numerical optimization are encouraged to apply. Publication of relevant results in conference proceedings and journals is expected. Capability of implementing the designs and algorithms in Matlab is expected; coding parts of the algorithms in C/C++ is a plus. The expected duration of the internship is 3-6 months and the start date is flexible.
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MD1405: Mechanism design for mobility
Mobility; externalities; mechanism design. We are looking for a talented and driven individual to help us design an efficient and equitable mobility solution. This position requires a deep understanding of mechanism design and at least some programming ability. Preference will be given to candidates with a background in transportation.
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MD1300: Compiler Optimizations for Linear Algebra Kernels
MERL is looking for a highly motivated individual to work on automatic, compiler based techniques for optimizing linear algebra kernels. The ideal candidate is a Ph.D. student in computer science with extensive experience in compiler design and source code optimization techniques. In particular, the successful candidate will have a strong working knowledge of polyhedral optimization techniques, the LLVM compiler, and Polly. Strong C/C++ skills and knowledge of LLVM at the source level are required. Publication of results in conference proceedings and journals is expected. The expected duration of the internship is 3 months and the start date is flexible.
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