Bridging this gap is difficult the complexities of modern CMP memory hierarchy make it even harder: Data cache becomes shared among computing units, and the sharing is often non-uniform-whether two computing units share a cache depends on their proximity and the level of the cache. Consequently, data movement and storage is expected to consume more than 70% of the total system power. It is expected that by 2018, node concurrency in an exascale system will increase by hundreds of times, whereas, memory bandwidth will expand by only 10 to 20 times.
Despite the capped growth of the peak CPU speed, the aggregate speed of a CMP keeps increasing as more cores get into a single chip. The second hardware more » trend is the growing gap between memory bandwidth and the aggregate speed-that is, the sum of all cores' computing power-of a Chip Multiprocessor (CMP). Some evidences are already shown on Graphic Processing Units (GPU): Irregular data accesses (e.g., indirect references A]) and conditional branches are limiting many GPU applications' performance at a level an order of magnitude lower than the peak of GPU. With more processors produced through a massive integration of simple cores, future systems will increasingly favor regular data-level parallel computations, but deviate from the needs of applications with complex patterns. The first is the increasing sensitivity of processors' throughput to irregularities in computation. The development of modern processors exhibits two trends that complicate the optimizations of modern software.
In our largest simulation, we have achieved 0.7 Petaflops/s of sustained performance on Jaguar.
Parallel gaussian software code#
Overall, the code has scaled on 256 CPU-GPUs on the Teragrid's Lincoln cluster and on 200,000 AMD cores of the Oak Ridge National Laboratory's Jaguar PF system. We have implemented and optimized the majority of the computation kernels on both Intel/AMD x86 and NVidia's Tesla/Fermi platforms for single and double floating point precision. We designed MOBO to support parallelism at all levels, including inter-node distributed memory parallelism, intra-node shared memory parallelism, data parallelism (vectorization), and fine-grained multithreading for GPUs.
Parallel gaussian software software#
The new method has been implemented in the software library MOBO (for 'Moving Boundaries'). Our approach has more » three distinct characteristics: (1) we faithfully represent the physics of RBCs by using nonlinear solid mechanics to capture the deformations of each cell (2) we accurately resolve the long-range, N-body, hydrodynamic interactions between RBCs (which are caused by the surrounding plasma) and (3) we allow for highly non-uniform spatial distributions of RBCs. In terms of the number of cells, we improve the state-of-the art by several orders of magnitude: the previous largest simulation, at the same physical fidelity as ours, resolved the flow of O(1,000-10,000) RBCs. The largest simulation amounts to 90 billion unknowns in space. We report simulations with up to 260 million deformable RBCs. Directly simulating blood is a challenging multiscale, multiphysics problem. Our goal is the direct simulation of blood, which we model as a mixture of a Stokesian fluid (plasma) and red blood cells (RBCs). We present a fast, petaflop-scalable algorithm for Stokesian particulate flows. Direct sequential computation of this sum would take O(N, We present fast adaptive parallel algorithms to compute the sum of N Gaussians at N points.
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