Intel recently introduced the Xeon Phi coprocessor based on the Many Integrated Core architecture featuring 60 cores with a peak performance of 1.0 Tflop/s. NASA has deployed a 128-node SGI Rackable system where each node has two Intel Xeon E2670 8-core Sandy Bridge processors along with two Xeon Phi 5110P coprocessors. We have conducted an early performance evaluation of the Xeon Phi. We used microbenchmarks to measure the latency and bandwidth of memory and interconnect, I/O rates, and the performance of OpenMP directives and MPI functions. We also used OpenMP and MPI versions of the NAS Parallel Benchmarks along with two production CFD applications to test four programming modes: offload, processor native, coprocessor native and symmetric (processor plus coprocessor). In this paper we present preliminary results based on our performance evaluation of various aspects of a Phi-based system.

Resource sharing in commodity multicore processors can have a significant impact on the performance of production applications. In this paper we use a differential performance analysis methodology to quantify the costs of contention for resources in the memory hierarchy of several multicore processors used in high-end computers. In particular, by comparing runs that bind MPI processes to cores in different patterns, we can isolate the effects of resource sharing. We use this methodology to measure how such sharing affects the performance of four applications of interest to NASA-OVERFLOW, MITgcm, Cart3D, and NCC. We also use a subset of the HPCC benchmarks and hardware counter data to help interpret and validate our findings. We conduct our study on high-end computing platforms that use four different quad-core microprocessors-Intel Clovertown, Intel Harpertown, AMD Barcelona, and Intel Nehalem-EP. The results help further our understanding of the requirements these codes place on their production environments and also of each computer's ability to deliver performance.

Suitability of the next generation of high-performance computing systems for petascale simulations will depend on a balance between factors such as processor performance, memory performance, local and global network performance, and Input/Output (I/O) performance. As the supercomputing industry develops new technologies for these subsystems, achieving system balance becomes challenging. In this paper, we evaluate the performance of a newly introduced dual-core-based SGI Altix 4700 system and we compare its performance with that of a single-core-based SGI Altix 3700 Bx2 system. We used the High-Performance Computing Challenge (HPCC) benchmarks and five real-world applications, three from computational fluid dynamics, one from climate modeling and one from nanotechnology. Our study shows that the SGI Altix 4700 performs slightly better than the SGI Altix 3700 Bx2 up to 128 processors, while the performance of the systems is almost the same beyond 128 processors, when the communication time dominates the compute time.

The suitability of next-generation high-performance computing systems for petascale simulations will depend on various performance factors attributable to processor, memory, local and global network, and input/output characteristics. In this paper, we evaluate performance of new dual-core SGI Altix 4700, quad-core SGI Altix ICE 8200, and dual-core IBM POWER5+ systems. To measure performance, we used micro-benchmarks from High Performance Computing Challenge (HPCC), NAS Parallel Benchmarks (NPB), and four real-world applications- three from computational fluid dynamics (CFD) and one from climate modeling. We used the micro-benchmarks to develop a controlled understanding of individual system components, then analyzed and interpreted performance of the NPBs and applications. We also explored the hybrid programming model (MPI+OpenMP) using multi-zone NPBs and the CFD application OVERFLOW-2. Achievable application performance is compared across the systems. For the ICE platform, we also investigated the effect of memory bandwidth on performance by testing 1, 2, 4, and 8 cores per node.

The integration of high-fidelity Computational Fluid Dynamics (CFD) analysis tools with the industrial design process benefits greatly from the robust implementations that are transportable across a wide range of computer architectures. In the present work, a hybrid domain-decomposition and parallelization concept was developed and implemented into the widely-used NASA multi-block Computational Fluid Dynamics (CFD) solvers employed in ENSAERO and OVERFLOW advanced flow analysis packages. These advanced engineering and scientific analysis packages include more than 300,000 lines of code written in FORTRAN 77 language in more than 1300 individual subprograms. The new parallel solver concept, PENS (Parallel Euler Navier-Stokes Solver), employs both fine and coarse granularity with data partitioning as well as data coalescing to obtain the desired load-balance characteristics on the available computer platforms for these legacy packages. This multi-level parallelism implementation itself introduces no changes to the numerical results, hence the original fidelity of the packages are identically preserved. The present implementation uses the Message Passing Interface (MPI) library for interprocessor message passing and memory accessing. By choosing an appropriate combination of the available partitioning and coalescing possibilities only during the execution stage, the PENS solver is used on different computer architectures from shared-memory to distributed-memory platforms with varying degrees of parallelism. Improvements in computational load-balance and speeds are extremely crucial on the realistic problems in the design of aerospace vehicles. The PENS implementation on the IBM SP2 distributed memory environment at the NASA Ames Research Center obtains 85 percent scalable parallel performance using fine-grain partitioning of single-block CFD domains using up to 128 wide computational nodes. Multi-block CFD simulations of complete aircraft geometries achieve 85 percent perfect load-balanced executions using data coalescing and the two levels of parallelism. SGI PowerChallenge, SGI Onyx2, and Cray T3E are the other platforms where the robustness, performance behavior, and the parallel scalability of the implementation are tested and fine-tuned for actual production run environments.