We enabled an unprecedented scale of quantum molecular dynamics simulations through algorithmic innovations. A new lean divide-and-conquer density functional theory algorithm significantly reduces the prefactor of the O(N) computational cost based on complexity and error analyses. A globally scalable and locally fast solver hybridizes a global real-space multigrid with local plane-wave bases. The resulting weak-scaling parallel efficiency was 0.984 on 786,432 IBM Blue Gene/Q cores for a 50.3 million-atom (39.8 trillion degrees-of-freedom) system. The time-to-solution was 60-times less than the previous state-of-the art, owing to enhanced strong scaling by hierarchical band-space domain decomposition and high floating-point performance (50.5% of the peak). Production simulation involving 16,661 atoms for 21,140 time steps (or 129,208 self-consistent-field iterations) revealed a novel nanostructural design for on-demand hydrogen production from water, advancing renewable energy technologies. This metascalable (or "design once, scale on new architectures") algorithm is used for broader applications within a recently proposed divide-conquer-recombine paradigm.

No abstract available.

Abstract The prime factor algorithm (PFA) is an efficient discrete Fourier transform (DFT) computation algorithm used when the sequence length can be decomposed into mutually prime factors. Following our previous results on PFA decomposition carried out at Caltech on hypercube machines, we present in the paper a pipeline PFA implementation suitable for multiprocessor systems with distributed memory. This implementation achieves high values of efficiency and speed‐up when processing multiple sequences of data. The paper shows how an optimized structure can be obtained when the concurrency among computation and communications is exploited at each node of the pipe. Experimental results obtained on transputer‐based structures and on the IntelTouchstoneDelta system are also reported.

This paper studies the behavior of scientific applications running on distributed memory parallel computers. Our goal is to quantify the floating point, memory, I/O and communication requirements of highly parallel scientific applications that perform explicit communication. In addition to quantifying these requirements for fixed problem sizes and numbers of processors, we develop analytical models for the effects of changing the problem size and the degree of parallelism for several of the applications. We use the results to evaluate the trade-offs in the design of multicomputer architectures.

Abstract This paper is a survey of activities related to parallel computing that took place primarily during the 1980s. The major areas covered are hardware, software, and performance measurement and characterization. Emphasis is on identifying the major milestones of the decade and on commercial computers rather than present a comprehensive survey of the field. The material is treated from the user's point of view.

the conference 'Parallel Computing: Achievements, Problems and Prospects' was held in Capri, Italy.The goals of the conference were: 0 to assess the progress made in the 1980s in parallel computation for scientific applications and to examine trends in large-scale computations as we enter the 1990s; 0 to provide an opportunity for discussion among the researchers and some of the well-known experts in the field of parallel computing.

Abstract Recently, a number of advanced architecture machines have become commercially available. These new machines promise better cost performance than traditional computers, and some of them have the potential of competing with current supercomputers, such as the CRAY X‐MP, in terms of maximum performance. This paper describes the methodology and results of a pilot study of the performance of a broad range of advanced architecture computers using a number of complete scientific application programs. The computers evaluated include: shared‐memory bus architecture machines such as the Alliant FX/8, the Encore Multimax, and the Sequent Balance and Symmetry shared‐memory network‐connected machines such as the Butterfly distributed‐memory machines such as the NCUBE, Intel and Jet Propulsion Laboratory (JPL)/Caltech hypercubes very long instruction word machines such as the Cydrome Cydra‐5 SIMD machines such as the Connection Machine ‘traditional’ supercomputers such as the CRAY X‐MP, CRAY‐2 and SCS‐40. Seven application codes from a number of scientific disciplines have been used in the study, although not all the codes were run on every machine. The methodology and guidelines for establishing a standard set of benchmark programs for advanced architecture computers are discussed. The CRAYs offer the best performance on the benchmark suite; the shared memory multiprocessor machines generally permitted some parallelism, and when coupled with substantial floating point capabilities (as in the Alliant FX/8 and Sequent Symmetry), provided an order of magnitude less speed than the CRAYs. Likewise, the early generation hypercubes studied here generally ran slower than the CRAYs, but permitted substantial parallelism from each of the application codes.