Browsing by Author "Stamatakis, Alexandros"
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- Dynamic Multigrain Parallelization on the Cell Broadband EngineBlagojevic, Filip; Nikolopoulos, Dimitrios S.; Stamatakis, Alexandros; Antonopoulos, Christos D. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2006)This paper addresses the problem of orchestrating and scheduling parallelism at multiple levels of granularity on heterogeneous multicore processors. We present policies and mechanisms for adaptive exploitation and scheduling of multiple layers of parallelism on the Cell Broadband Engine. Our policies combine event-driven task scheduling with malleable loop-level parallelism, which is exposed from the runtime system whenever task-level parallelism leaves cores idle. We present a runtime system for scheduling applications with layered parallelism on Cell and investigate its potential with RAxML, a computational biology application which infers large phylogenetic trees, using the Maximum Likelihood (ML) method. Our experiments show that the Cell benefits significantly from dynamic parallelization methods, that selectively exploit the layers of parallelism in the system, in response to workload characteristics. Our runtime environment outperforms naive parallelization and scheduling based on MPI and Linux by up to a factor of 2.6. We are able to execute RAxML on one Cell four times faster than on a dual-processor system with Hyperthreaded Xeon processors, and 5--10\% faster than on a single-processor system with a dual-core, quad-thread IBM Power5 processor.
- The iPlant collaborative: cyberinfrastructure for plant biologyGoff, Stephen A.; Vaughn, Matthew; McKay, Sheldon; Lyons, Eric; Stapleton, Ann E.; Gessler, Damian; Matasci, Naim; Wang, Liya; Hanlon, Matthew; Lenards, Andrew; Muir, Andy; Merchant, Nirav; Lowry, Sonya; Mock, Stephen; Helmus, Matthew R.; Kubach, Adam; Narro, Martha; Hopkins, Nicole; Micklos, David; Hilgert, Uwe; Gonzales, Michael; Jordan, Chris; Skidmore, Edwin; Dooley, Rion; Cazes, John; McLay, Robert; Lu, Zhenyuan; Pasternak, Shiran; Koesterke, Lars; Piel, William H.; Grene, Ruth; Noutsos, Christos; Gendler, Karla; Feng, Xin; Tang, Chunlao; Lent, Monica; Kim, Seung-Jin; Kvilekval, Kristian; Manjunath, B. S.; Tannen, Val; Stamatakis, Alexandros; Sanderson, Michael; Welch, Stephen M.; Cranston, Karen A.; Soltis, Pamela S.; Soltis, Douglas E.; O'Meara, Brian; Ane, Cecile; Brutnell, Tom; Kleibenstein, Daniel J.; White, Jeffery W.; Leebens-Mack, James H.; Donoghue, Michael J.; Spalding, Edgar P.; Vision, Todd J.; Myers, Christopher R.; Lowenthal, David K.; Enquist, Brian J.; Boyle, Brad; Akoglu, Ali; Andrews, Greg; Ram, Sudha; Ware, Doreen; Stein, Lincoln; Stanzione, Dan (Frontiers, 2011)The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.
- RAxML-Cell: Parallel Phylogenetic Tree Inference on the Cell Broadband EngineBlagojevic, Filip; Stamatakis, Alexandros; Antonopoulos, Christos D.; Nikolopoulos, Dimitrios S. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2006)Phylogenetic tree reconstruction is one of the grand challenge problems in Bioinformatics. The search for a best-scoring tree with 50 organisms, under a reasonable optimality criterion, creates a topological search space which is as large as the number of atoms in the universe. Computational phylogeny is challenging even for the most powerful supercomputers. It is also an ideal candidate for benchmarking emerging multiprocessor architectures, because it exhibits various levels of fine and coarse-grain parallelism. In this paper, we present the porting, optimization, and evaluation of RAxML on the Cell Broadband Engine. RAxML is a provably efficient, hill climbing algorithm for computing phylogenetic trees based on the Maximum Likelihood (ML) method. The algorithm uses an embarrassingly parallel search method, which also exhibits data-level parallelism and control parallelism in the computation of the likelihood functions. We present the optimization of one of the currently fastest tree search algorithms, on a real Cell blade prototype. We also investigate problems and present solutions pertaining to the optimization of floating point code, control flow, communication, scheduling, and multi-level parallelization on the Cell.