First experience of compressible gas dynamics simulation on the Los Alamos roadrunner machine

“…This unusual grid size is a result of our having left much of the code outside of the advection kernel unchanged from its gas dynamics implementation. Therefore, each grid briquette of 4 3 cells has a contiguous record in main memory of 5u4 3 words, corresponding to a sequence of records for a density, pressure, and 3 components of velocity. We read and write only the first record, for the density, in this application, but the space for the other 4 remains, and these 4 additional blank records are incorporated into all the MPI message passing.…”

Section: Advection Examplementioning

confidence: 99%

“…It also shares with our full application a very important feature: it has a large difference stencil, and the computation proceeds in phases that alternate between computations of cell and cell interface quantities. Each of these alternating phases of computation becomes transformed in our high performance code expression into a separate stage in a computation pipeline (see [2][3][4][5]). In this way, this PPM advection kernel reflects the overall character of our full application.…”

Section: Advection Examplementioning

confidence: 99%

A Study of the Performance of Multifluid PPM Gas Dynamics on CPUs and GPUs

Lin

Jayaraj

Woodward³

2011

2011 Symposium on Application Accelerators in High-Performance Computing

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The potential for GPUs and many-core CPUs to support high performance computation in the area of computational fluid dynamics (CFD) is explored quantitatively through the example of the PPM gas dynamics code with PPB multifluid volume fraction advection. This code has already been implemented on the IBM Cell processor and run at full scale on the Los Alamos Roadrunner machine. This implementation has involved a complete restructuring of the code that has been described in detail elsewhere. Here the lessons learned from that work are exploited to take advantage of today's latest generations of multi-core CPUs and many-core GPUs. The operations performed by this code are characterized in detail after being first decomposed into a series of individual code kernels to allow an implementation on GPUs. Careful implementations of this code for both CPUs and GPUs are then contrasted from a performance point of view. In addition, a single kernel that has many of the characteristics of the full application on CPUs has been built into a full, standalone, scalable parallel application. This single-kernel application shows the GPU at its best. In contrast, the full multifluid gas dynamics application brings into play computational requirements that highlight the essential differences in CPU and GPU designs today and the different programming strategies needed to achieve the best performance for applications of this type on the two devices. The single kernel application code performs extremely well on both platforms. This application is not limited by main memory bandwidth on either device; instead it is limited only by the computational capability of each. In this case, the GPU has the advantage, because it has more computational cores. The full multifluid gas dynamics code is, however, of necessity memory bandwidth limited on the GPU, while it is still computational capability limited on the CPU. We believe that these codes provide a useful context for quantifying the costs and benefits of design decisions for these powerful new computing devices. Suggestions for improvements in both devices and codes based upon this work are offered in our conclusions.GPGPU, multicore CPU, high-performance computing, exascale computing, computational fluid dynamics, parallel programming, source-to-source transformation.

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“…The indexing of these vector temporaries is baroque (cf. [4][5][6]). The programming effort required to produce such a code, modify it, debug it, and maintain it is excessive.…”

Section: The Solution: Extreme Pipelining Of the Computationmentioning

confidence: 99%

Boosting the performance of computational fluid dynamics codes for interactive supercomputing

Woodward

Jayaraj

Lin

et al. 2010

Procedia Computer Science

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An extreme form of pipelining of the Piecewise-Parabolic Method (PPM) gas dynamics code has been used to dramatically increase its performance on the new generation of multicore CPUs. Exploiting this technique, together with a full integration of the several data post-processing and visualization utilities associated with this code has enabled numerical experiments in computational fluid dynamics to be performed interactively on a new, dedicated system in our lab, with immediate, user controlled visualization of the resulting flows on the PowerWall display. The code restructuring required to achieve the necessary CPU performance boost, as well as the parallel computing methods and systems used to enable interactive flow simulation are described. Requirements for these techniques to be applied to other codes are discussed, and our plans for tools that will assist programmers to exploit these techniques are briefly described. Examples showing the capability of the new system and software are given for applications in turbulence and stellar convection.

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First experience of compressible gas dynamics simulation on the Los Alamos roadrunner machine

Cited by 12 publications

References 31 publications

Two-step hybrid conservative remapping for multimaterial arbitrary Lagrangian–Eulerian methods

Two-step hybrid conservative remapping for multimaterial arbitrary Lagrangian–Eulerian methods

A Study of the Performance of Multifluid PPM Gas Dynamics on CPUs and GPUs

Boosting the performance of computational fluid dynamics codes for interactive supercomputing

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