Implementing Wilson-Dirac Operator on the Cell Broadband Engine

Computing the actions of Wilson-Dirac operators consumes most of the CPU time for the grand challenge problem of simulating Lattice Quantum Chromodynamics (Lattice QCD). This routine exhibits many challenges to implementation on most computational environments because of the multiple pattern of accessing the same data that make it difficult to align the data efficiently at compile time. Additionally, the low computation to memory access ratio makes this computation both memory bandwidth and memory latency bounded. In this work, we present an implementation of this routine on Cell Broadband Engine. We propose runtime data fusion, an approach aiming at aligning data at runtime, for data that cannot be aligned optimally at compile time, to improve SIMDized execution. We also show DMA optimization technique that reduces the impact of BW limits on performance. Our implementation for this routine achieves 31.2 GFlops for single precision computations and 8.75 GFlops for double precision computations

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

Monte Carlo Simulations of Spin Glasses on Cell Broadband Engine

Several large-scale computational scientific problems require high-end computing systems to be solved. In the recent years, design of multi-core architectures delivers on a single chip tens or hundreds Gflops of peak computing performance, with high power dissipation efficiency, and it makes available computational power previously available only on high-end multi-processor systems. The aim of this Ph.D. thesis is to study the capability of multi-core processors for scientific programming, analyzing sustained performance, issues related to multicore programming, data distribution, synchronization, in order to define a set of guideline rules to optimize scientific applications for this class of architectures. As an example of multi-core processor, we consider the Cell Broadband Engine (CBE), developed by Sony, IBM and Toshiba. The CBE is one of the most powerful multi-core CPU current available, integrating eight cores and delivering a peak performance of 200 Gflops in single precision and 100 Gflops in double precision. As case of study, we analyze the performances of CBE for Monte Carlo simulations of the Edwards-Anderson Spin Glass model, a paradigm in theoretical and condensed matter physics, used to describe complex systems characterized by phase transitions (such as the para-ferro transition in magnets) or model “frustrated” dynamics. We descrive several strategies for the distribution of data set among on-chip and off-chip memories and propose analytic models to find out the balance between computational and memory access time as a function of both algorithmic and architectural parameters. We use the analytic models to set the parameters of the algorithm, like for example size of data structures and scheduling of operations, to optimize execution of Monte Carlo spin glass simulations on the CBE architecture

Implementing Wilson-Dirac Operator on the Cell Broadband Engine

Computing the actions of Wilson-Dirac operators consumes most of the CPU time for the grand challenge problem of simulating Lattice Quantum Chromodynamics (Lattice QCD). This routine exhibits many challenges to implementation on most computational environments because of the multiple pattern of accessing the same data that make it difficult to align the data efficiently at compile time. Additionally, the low computation to memory access ratio makes this computation both memory bandwidth and memory latency bounded. In this work, we present an implementation of this routine on Cell Broadband Engine. We propose runtime data fusion, an approach aiming at aligning data at runtime, for data that cannot be aligned optimally at compile time, to improve SIMDized execution. We also show DMA optimization technique that reduces the impact of BW limits on performance. Our implementation for this routine achieves 31.2 GFlops for single precision computations and 8.75 GFlops for double precision computations