Optimizing Web Virtual Reality. Rabimba Karanjai. M.S. Thesis, December 2017.
Exploration of Supervised Machine Learning Techniques for Runtime Selection of CPU vs. GPU Execution in Java Programs. Gloria Kim, Akihiro Hayashi, Vivek Sarkar. Fourth Workshop on Accelerator Programming Using Directives (WACCPD), November 2017. (co-located with SC17)
Chapel-on-X: Exploring Tasking Runtimes for PGAS Languages. Akihiro Hayashi, Sri Raj Paul, Max Grossman, Jun Shirako, Vivek Sarkar. Third IEEE Workshop on Extreme Scale Programming Models and Middleware (ESPM2), November 2017. (co-located with SC17)
Deadlock Avoidance in Parallel Programs with Futures: Why parallel tasks should not wait for strangers. Tiago Cogumbreiro, Rishi Surendran, Francisco Martins, Vivek Sarkar, Vasco T. Vasconcelos, and Max Grossman. In ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA). ACM, 2017.
Exploring Compiler Optimization Opportunities for the OpenMP 4.x Accelerator Model on a POWER8+GPU Platform. Akihiro Hayashi, Jun Shirako, Ettore Tiotto, Robert Ho, Vivek Sarkar. Third Workshop on Accelerator Programming Using Directives (WACCPD, co-located with SC16), November 2016.
Optimized Distributed Work-Stealing. Vivek Kumar, Karthik Murthy, Vivek Sarkar and Yili Zheng. 6th workshop on Irregular Applications: Architectures and Algorithms (IA^3), ACM, November 2016 [slides].
Automatic Parallelization of Pure Method Calls via Conditional Future Synthesis. Rishi Surendran and Vivek Sarkar. 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA 2016), November 2016.
Pedagogy and Tools for Teaching Parallel Computing at the Sophomore Undergraduate Level. Max Grossman, Maha Aziz, Heng Chi, Anant Tibrewal, Shams Imam, Vivek Sarkar. Journal of Parallel and Distributed Computing Special Issue on Parallel, Distributed, and High Performance Computing Education. 2016.
OpenMP as a High-Level Specification Language for Parallelism. Max Grossman, Jun Shirako, Vivek Sarkar. International Workshop on OpenMP (IWOMP), October 2016.
An Extended Polyhedral Model for SPMD Programs and its use in Static Data Race Detection. Prasanth Chatarasi, Jun Shirako, Martin Kong, Vivek Sarkar. The 29th International Workshop on Languages and Compilers for Parallel Computing (LCPC), September 2016 [slides].
The Open Community Runtime: A Runtime System for Extreme Scale Computing. Timothy G. Mattson, Romain Cledat, Vincent Cave, Vivek Sarkar, Zoran Budimlic, Sanjay Chatterjee, Josh Fryman, Ivan Ganev, Robin Knauerhase, Min Lee, Benoıt Meister, Brian Nickerson, Nick Pepperling, Bala Seshasayee, Sagnak Tasirlar, Justin Teller, Nick Vrvilo. In 2016 IEEE High Performance Extreme Computing Conference (HPEC ’16).
Dynamic Determinacy Race Detection for Task Parallelism with Futures. Rishi Surendran and Vivek Sarkar. 16th International Conference on Runtime Verification (RV'16), September 2016.
Declarative Tuning for Locality in Parallel Programs. Sanjay Chatterjee, Nick Vrvilo, Zoran Budimlic, Kathleen Knobe, Vivek Sarkar. The 45th International Conference on Parallel Processing (ICPP-2016), August 2016. (slides)
Integrating Asynchronous Task Parallelism with OpenSHMEM. Max Grossman, Vivek Kumar, Zoran Budimlic, Vivek Sarkar. OpenSHMEM Workshop, August 2016.
Brief Announcement: Dynamic Determinacy Race Detection for Task Parallelism with Futures. Rishi Surendran and Vivek Sarkar. 28th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), July 2016.
SWAT: A Programmable, In-Memory, Distributed, High-Performance Computing Platform International ACM Symposium on High-Performance Parallel and Distributed Computing
Automatic Data Layout Generation and Kernel Mapping for CPU+GPU Architectures. DeepakMajeti, KuldeepMeel, RajBarik and Vivek Sarkar. 25th International Conference on Compiler Construction (CC 2016), March 2016.
Efficient Static and Dynamic Memory Management Techniques for Multi-GPU System. Max Grossman, Mauricio Araya-Polo. Workshop on Runtime Systems for Extreme Scale Programming Models and Architectures. November 2015.
Distributed, Heterogeneous Scheduling Techniques Motivated by Production Geophysical Applications. Max Grossman, Mauricio Araya-Polo. Workshop on Many-Task Computing on Clouds, Grids, and Supercomputer. November 2015.
Concurrent Collections. Kathleen Knobe, Michael G. Burke, and Frank Schlimbach. Programming Models for Parallel Computing, Chapter 11, pages 247-280. Pavan Balaj, editor. The MIT Press, November 2015.
Optimized Event-Driven Runtime Systems for Programmability and Performance. Sagnak Tasirlar. Ph.D. Thesis, October 2015.
Extending Polyhedral Model for Analysis and Transformations of OpenMP Programs. Prasanth Chatarasi, and Vivek Sarkar. PACT ACM Student Research Competition, October 2015. [accepted as poster with accompanying extended abstract][poster].
Race Detection in Two Dimensions. Dimitar Dimitrov, Martin Vechev, Vivek Sarkar. 27th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA), June 2015.
Cooperative Execution of Parallel Tasks with Synchronization Constraints. Shams Imam. Ph.D. Thesis, May 2015.
DFGR: an Intermediate Graph Representation for Macro-Dataflow Programs. Alina Sbirlea, Louis-Noel Pouchet, Vivek Sarkar. Fourth Workshop on Dataflow Execution Models for Extreme Scale Computing - in conjunction with PACT 2014 (DFM 2014)
Determinacy and Repeatability of Parallel Program Schemata. Jack B. Dennis, Guang R. Gao, Vivek Sarkar. Workshop on Data-Flow Execution Models for Extreme Scale Computing (DFM 2012).
Report on Inter-Agency Workshop on HPC Resilience at Extreme Scale. (Editor: John T. Daly.) February 2012.
Array Optimizations for High Productivity Programming Languages. Mackale Joyner. Ph.D. Thesis, September 2008.
This material is based upon work supported by the National Science Foundation under Grants No. 0833166, 0938018, 0926127, 0964520, 1302570. Anyopinions,findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).