Performance, Efficiency, Autonomy, and Reliability in Computing Systems
The main goal of our Research Lab is to derive unique insights on and improve the performance, efficiency, and reliability of large-scale computing systems. Our research questions and revisits long-standing concepts and abstractions in system software, in the context of technology trends and workload evolution. We use experimental and theoretical approaches to enable autonomic management of system resources. Our current emphasis is on the automated synthesis of memory management algorithms and data structures, new abstractions and architectures for Cloud Computing and taming extreme heterogeneity in High Performance Computing.
HETEROGENEOUS SCALABLE MEMORY SYSTEMS
Managing inherent and architected heterogeneity in current and future memory systems. Revisiting virtual memory management abstractions and algorithms in light of new memory technologies and emerging workloads.
Introducing new abstractions and capabilities for Functions-as-a-Service to add hardware diversity and enable higher performance. Supported by NSF
DEMOCRATIZING MACHINE LEARNING TRAINING
Research on enabling training of ML models on any device, anywhere.
SYNTHESIS OF HIGH-PERFORMANCE DATA STRUCTURES
Rethinking how data structures for high-performance computing are designed and explore new data structure synthesis methodologies.
Professor of Computer Science and by courtesy Electrical and Computer Engineering, working in system software and high-performance computing. Current interests include memory systems and new approaches to Cloud Computing.
Working on rethinking virtual memory management in the context of new software and hardware technologies.
Working on virtualization technologies for future wireless networks
Working on emerging heterogeneous memory technologies
Designed and implemented the first capability for ephemeral communication and stateful sharing in serverless computing, prototyped on AWS Lambda.
Rethinking page migration in Linux in an era of extreme heterogeneity and non-volatility in server memory systems.
MRUNAL MANISH KHINVASARA
Working on algorithmic and system improvement for large-scale graph mining systems.
Working on new educational tools and visualization technologies for teaching computing systems concepts in CS curricula.
RESEARCH ASSISTANTS (ALL LEVELS)
We have a mission to develop young talent in Computing Systems Research and we are looking for new students to join our team at all levels (PhD, MSc, or Undergraduate). You will have excellent opportunities and state of the art equipment to address challenging research problems and shape the future of large-scale computing systems. We offer an open, collaborative and conducive research environment. We have trained more than fifty (50) PostDoctoral Fellows, PhD Students, MSc students, and Undergraduates, all of whom are now in senior and leadership positions in academia and industry. Our researchers benefit from our strong network of research partners in government and industry and have extensive opportunities for internships and joint research.