Ruizhe Li

Efficient Nonserial Polyadic Dynamic Programming on the Cell Processor

Dynamic programming (DP) is an effective technique for many search and optimization problems. However, the high arithmetic complexity limits its extensive use. Although modern processor architectures with multiple cores and SIMD (single instruction multiple data) instructions provide increasingly high computing power, even the state-of-the-art fully optimized algorithm still largely underutilizes modern multi-core processors. In this paper we propose to improve one family of DP, nonserial polyadic DP (NPDP), targeting a heterogeneous multi-core architecture, the Cell Broadband Engine. We first design a new data layout which efficiently utilizes the on-chip memory system of the Cell processor. Next we devise a CellNPDP algorithm with two tiers. The first tier is a SPE (a co-processor on the Cell processor) procedure which efficiently computes a block of data that can fit into one SPEs local store. The second tier is a parallel procedure which enables all SPEs to efficiently compute all blocks of data. To evaluate CellNPDP, we use both performance modeling and experiments. The performance model reveals that the processor utilization of NPDP can be independent of the problem size. To empirically evaluate CellNPDP, we use two platforms: the IBM QS20 dual-Cell blade and a CPU platform with two latest quad-core CPUs. On both platforms, the processor utilization of CellNPDP is larger than 60%, which demonstrates that our optimizations and CellNPDP can be architecture-independent. Compared to the state-of-the-art fully optimized algorithm on the CPU platform, CellNPDP is 44-fold faster for single-precision and 28-fold faster for double-precision, which is a significant improvement to NPDP.

Improving Parallel Performance of a Finite-Difference AGCM on Modern High-Performance Computers

AbstractThe rapid development of science and technology has enabled finer and finer resolutions in atmospheric general circulation models (AGCMs). Parallelization becomes progressively more critical as the resolution of AGCMs increases. This paper presents a new parallel version of the finite-difference Gridpoint Atmospheric Model of the Institute of Atmospheric Physics (IAP)–State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG; GAMIL) with various parallel optimization strategies, including two-dimensional hybrid parallel decomposition; hybrid parallel programming; parallel communications for coupling the physical packages, land surface, and dynamical core; and a cascading solution to the tridiagonal equations used in the dynamical core. The new parallel version under two different horizontal resolutions (1° and 0.25°) is evaluated. The new parallel version enables GAMIL to achieve higher parallel efficiency and utilize a greater number of CPU cores. GA...

C-Coupler2: a flexible and user-friendly community coupler for model coupling and nesting

The Chinese C-Coupler (Community Coupler) family aims primarily to develop coupled models for weather forecasting and climate simulation and prediction. It is targeted to serve various coupled models with flexibility, userfriendliness, and extensive coupling functions. C-Coupler2, the latest version, includes a series of new features in addition to those of C-Coupler1 – including a common, flexible, and user-friendly coupling configuration interface that combines a set of application programming interfaces and a set of XML-formatted configuration files; the capability of coupling within one executable or the same subset of MPI (message passing interface) processes; flexible and automatic coupling procedure generation for any subset of component models; dynamic 3-D coupling that enables convenient coupling of fields on 3-D grids with time-evolving vertical coordinate values; non-blocking data transfer; facilitation for model nesting; facilitation for increment coupling; adaptive restart capability; and finally a debugging capability. C-Coupler2 is ready for use to develop various coupled or nested models. It has passed a number of test cases involving model coupling and nesting, and with various MPI process layouts between component models, and has already been used in several real coupled models.

CSAP: A Performance Predictor for Climate Simulation Applications on Intel CPUs

The development of climate simulation applications highly depends on high-performance computers. A fundamental problem in constructing a high-performance computer is how to select the processors. A straightforward solution is to use a state-of-the-art processor version available from the market. However, this approach is not economical for climate simulation applications.

Performance Characterization and Optimization for Intel Xeon Phi Coprocessor

The Intel Xeon Phi is a many-core accelerator which focuses on the high performance applications. To characterize the performance of the Intel Xeon Phi, a system of dual 8-core Intel Xeon E5-2670 processors is employed as a control platform, and a subset of the PARSEC benchmark suite is selected as the benchmark applications. The first evaluation in this paper shows that the applications on the Intel Xeon Phi is averagely 2.06x slower than on the dual Intel Xeon E5-2670. The further detailed performance characterization quantifies the performance impact of various architecture parameters on the Intel Xeon Phi. To set an example for how to improve the architecture of the Intel Xeon Phi for better performance, the hardware optimization with an additional set of vector processing units is discussed and a simple emulator is developed accordingly. The evaluation results show that this optimization can provide an average speedup of 1.10.