Abstract:
In this Forum, I will present how to develop faster, cheaper and better linear algebra software for GPUs through a hybridization methodology that is built on (1) Representing linear algebra algorithms as directed acyclic graphs where nodes correspond to tasks and edges to dependencies among them, and (2) Scheduling the execution of the tasks over hybrid architectures of GPUs and multicore. The methodology is successfully used in MAGMA - a new generation of linear algebra libraries, similar in functionality to LAPACK, but extended for hybrid, GPU-based systems. Complex algorithms can be expressed through sequential-like code, based on computational tasks that are often already available, e.g., through optimized CPU/GPU BLAS, LAPACK, PLASMA, etc. libraries. Data-driven parallelism can then be extracted (implicitly) from the high-level description of the algorithm using run-time systems (e.g., DAGuE, StarPU, Quark, etc.) for scheduling the execution of the different tasks over the hybrid processing units. Resulting productivity is then fast and cheap, as the high-level development uses existing software infrastructure. Moreover, the resulting hybrid algorithms are better performance-wise than corresponding homogeneous algorithms designed exclusively for either GPUs or homogeneous multicore CPUs.

Bio:

Jack Dongarra received a Bachelor of Science in Mathematics from Chicago State University in 1972 and a Master of Science in Computer Science from the Illinois Institute of Technology in 1973. He received his Ph.D. in Applied Mathematics from the University of New Mexico in 1980. He worked at the Argonne National Laboratory until 1989, becoming a senior scientist.
He now holds an appointment as University Distinguished Professor of Computer Science in the Computer Science Department at the University of Tennessee, has the position of a Distinguished Research Staff member in the Computer Science and Mathematics Division at Oak Ridge National Laboratory (ORNL), Turing Fellow in the Computer Science and Mathematics Schools at the University of Manchester, and an Adjunct Professor in the Computer Science Department at Rice University.

He specializes in numerical algorithms in linear algebra, parallel computing, the use of advanced-computer architectures, programming methodology, and tools for parallel computers. His research includes the development, testing and documentation of high quality mathematical software.
He has contributed to the design and implementation of the following open source software packages and systems: EISPACK, LINPACK, the BLAS, LAPACK, ScaLAPACK, Netlib, PVM, MPI, NetSolve, Top500, ATLAS, and PAPI. He has published approximately 200 articles, papers, reports and technical memoranda and he is coauthor of several books.
He was awarded the IEEE Sid Fernback Award in 2004 for his contributions in the application of high performance computers using innovative approaches and in 2008 he was the recipient of the first IEEE Medal of Excellence in Scalable Computing; in 2010 he was the first recipient of the SIAM Special Interest Group of Supercomputing’s award for Career Achievement. He is a fellow of the AAAS, ACM, IEEE, and SIAM and a member of the national Academy of Engineering.
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