14 research outputs found
Dynamic Multigrain Parallelization on the Cell Broadband Engine
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
RAxML-Cell: Parallel Phylogenetic Tree Inference on the Cell Broadband Engine
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
Multilayered Heterogeneous Parallelism Applied to Atmospheric Constituent Transport Simulation
Heterogeneous multicore chipsets with many levels of parallelism are becoming increasingly common in high-performance computing systems. Effective use of parallelism in these new chipsets constitutes the challenge facing a new generation of large scale scientific computing applications. This study examines methods for improving the performance of two-dimensional and three-dimensional atmospheric constituent transport simulation on the Cell Broadband Engine Architecture (CBEA). A function offloading approach is used in a 2D transport module, and a vector stream processing approach is used in a 3D transport module. Two methods for transferring incontiguous data between main memory and accelerator local storage are compared. By leveraging the heterogeneous parallelism of the CBEA, the 3D transport module achieves performance comparable to two nodes of an IBM BlueGene/P, or eight Intel Xeon cores, on a single PowerXCell 8i chip. Module performance on two CBEA systems, an IBM BlueGene/P, and an eight-core shared-memory Intel Xeon workstation are given