Browsing by Author "Antonopoulos, Christos D."

Now showing 1 - 4 of 4
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    Dynamic Multigrain Parallelization on the Cell Broadband Engine
    Blagojevic, 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.
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    OpenDwarfs: Characterization of Dwarf-Based Benchmarks on Fixed and Reconfigurable Architectures
    Krommydas, Konstantinos; Feng, Wu-chun; Antonopoulos, Christos D.; Bellas, N. (2016-12)
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    Prediction-based Power-Performance Adaptation of Multithreaded Scientific Codes
    Curtis-Maury, Matthew; Blagojevic, Filip; Antonopoulos, Christos D.; Nikolopoulos, Dimitrios S. (Department of Computer Science, Virginia Polytechnic Institute & State University, 2007)
    Computing is currently at an inflection point, with the degree of on-chip thread-level parallelism doubling every one to two years. The number of cores has become one of the most important architectural parameters that characterize performance and power-efficiency of a modern microprocessor, and a computer system in general. Concurrency lends itself naturally to allowing a program to trade some of its performance for power savings, by regulating the number of active cores. Unfortunately, in several computing domains, users are unwilling to sacrifice performance to save power. Futhermore, the opportunities for saving power via other means, such as voltage and frequency scaling, may be limited in heavily optimized applications. In this paper, we present a prediction model for identifying energy-efficient operating points of concurrency in well-tuned multithreaded scientific applications, and a runtime system which uses live analysis of hardware event rates through the prediction model, to optimize applications dynamically. The runtime system throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each parallel execution phase. We present a dynamic, phase-aware performance prediction model (DPAPP), which combines multivariate regression techniques with runtime analysis of data collected from hardware event counters, to locate optimal operating points of concurrency. DPAPP is hardware-aware, in the sense that it takes into account the dimensions of parallelism in the architecture, using distinct predictors and hardware events for each dimension. It is also phase-aware. Using DPAPP, we develop a prediction-driven runtime optimization scheme, which drastically reduces the overhead of searching the optimization space for power-performance efficiency, while achieving near-optimal performance and power savings in real parallel applications.
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    RAxML-Cell: Parallel Phylogenetic Tree Inference on the Cell Broadband Engine
    Blagojevic, 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.