Codebase release 1.3 for SymTensor

We address a key challenge in programming high-performance applications - achieving portable performance, i.e., the same source code achieves a consistent, high level of performance over the variety of modern parallel processors, including multi-core CPU and many-core Graphics Processing Unit (GPU), and over the variety of input sizes. Our approach relies on the algebraic formalism of Multi-Dimensional Homomorphisms (MDH), which enables expressing data-parallel computations uniformly via a higher-order function (a.k.a. parallel pattern). For MDHs, we develop a novel code generation approach based on a generic OpenCL implementation. Our implementation efficiently exploits the OpenCL's abstract platform and memory model, generically for arbitrary MDH functions, by incorporating a parameterized parallelization and tiling strategy - on both layers of the OpenCL's two models and in all dimensions of the multi-dimensional input. We achieve performance portability for MDHs by auto-tuning the parameters of our two strategies, thereby enabling fully automatically optimizing our code for any combination of an MDH function, target device, and input size. We demonstrate for computations from four popular domains - dense linear algebra (BLAS), stencil computations, data mining, and tensor contractions - how we express them in the MDH formalism, and we experimentally show that our automatically generated and auto-tuned code for them achieves competitive and often significantly better performance than several state-of-practice approaches on both Intel multi-core CPU and NVIDIA many-core GPU - speedups of up to 5x over the state-of-the-art performance-portable approaches, and competitive or even better performance as compared to hand-optimized approaches such as Intel MKL and NVIDIA cuBLAS on real-world input data as used in deep learning.

Large-scale datasets in computational chemistry typically require distributed-memory parallel methods to perform a special operation known as tensor contraction. Tensors are multidimensional arrays, and a tensor contraction is akin to matrix multiplication with special types of permutations. Creating an efficient algorithm and optimized im- plementation in this domain is complex, tedious, and error-prone. To address this, we develop a notation to express data distributions so that we can apply use automated methods to find optimized implementations for tensor contractions. We consider the spin-adapted coupled cluster singles and doubles method from computational chemistry and use our methodology to produce an efficient implementation. Experiments per- formed on the IBM Blue Gene/Q and Cray XC30 demonstrate impact both improved performance and reduced memory consumption.

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

In my last column (see ibid., January/February 2001), I talked about a notational device for matrix algebra called tensor diagrams. This time I write some C++ code to symbolically evaluate these quantities. This gives me a chance to play with some as yet untried features in the C++ standard library, such as strings and standard template library (STL) container classes.

Ab initio determination of molecular geometries and vibrational frequencies of CX 3 COOH (X = H, F, Cl, Br)

Using Gaussian 05 Revision C.02 version of the quantum chemical program ab initio computations has been carried out to compute optimized geometry, harmonic vibrational frequencies along with intensities in IR and Raman spectra and atomic charges at rhf / 6–31+g*, b3lyp / 6–31++g** and b3lyp / 6–311++g** levels. The optimized molecular structure is found to possess Cs point group symmetry with the plane of symmetry passing through the C atom, to which the CF3 group is attached, the centrally opposite C atom and the C atom of the CF3 group and perpendicular to the phenyl ring plane. One of the three F atoms of the CF3 group is found to be on one side of the ring plane and is situated on the plane of the symmetry while the other two F atoms are found to be on the other side of the ring plane placed symmetrically on either side of the symmetry plane. Vibrational assignments for the normal modes have been reviewed. To make vibrational assignments for the normal modes of vibration the Gauss View software was used. The CF3 group has the characteristic frequencies in the 340, 768, 1072, 1152 and 1318 cm-1 which are due to the modes δs (CF3), Vs (CF3 ), Vas(CF3) (a’), Vas(CF3) (a”) and V(C–CF3 ) respectively. The torsional frequency of the CF3 group is found to be 16 cm-1 which would lead to barrier height of ~ 0.5 kJ / mol for the torsional motion of the CF3 group.

CheMPS2: A free open-source spin-adapted implementation of the density matrix renormalization group for ab initio quantum chemistry

H.26L is an emerging video encoding standard proposed by ITU, which goal is for video communication and multicast in low bit rate conditions. We give detailed analysis and performance tests for the parallelism of the H.26L video encoder test model long-term (TML8.0). Based on these tests, several parallel schedule strategies are discussed in different aspects, and the optimal one is selected and implemented by using MPI. At last, experiment results on Dawning-2000 MPP machines with different processors are given. The analysis and simulation results show that the H.26L has a good parallel performance and scalability on slice layer, and parallel computing can greatly speed up the processing of H.26L encoder. The experiment methods and results can be applied to many parallel systems for H.26L video processing.

Many studies have shown that there is a direct relationship between Single Nucleotide Polymorphisms (SNPs) and the appearance of complex diseases, such as Alzheimer’s or Parkinson’s. However, recent advances in the Study of the Complete Genome Association indicate that the relationship between SNPs and these diseases goes beyond a simple one-to-one relationship, that is, the appearance of multiple SNPs (epistasis) influences the appearance of these diseases. In this sense, this work proposes the application of the NSGA-II multi-objective algorithm for the detection of epistasis of multiple loci in a database with 31,341 SNPs. Moreover, a parallel study has been performed to reduce the execution time of this problem. Our implementation not only achieves a reasonable good parallel performance and scalability, but also its biological significance overcomes other approaches published in the literature.

The pattern design of superhydrophobic surfaces can be significantly aided by computations that predict the Cassie-Baxter (CB) to Wenzel (W) transition, which is responsible for the break-down of superhydrophobic behavior. We present a computational framework for the optimization of patterned surfaces based on the energy barriers of the CB-W transitions which comprises the following elements: (a) design of structured surface patterns, for example, arrays of pillars, with parameterized geometric features such as size, pitch, slope, and roundness. (b) Computation of the wetting states with a modified Young-Laplace equation that facilitates the introduction of solid/liquid interactions for complex surface patterns and has significantly lower computational cost than other commonly used methods, such as the volume-of-fluid, phase-field, and so forth. (c) Incorporation of the modified Young-Laplace in the simplified string method, allowing the calculation of the minimum energy paths of wetting transitions which, apart from the energy barriers, also reveal the transition mechanisms (CB failure modes). (d) Accommodation of large-scale problems with good parallel performance and scalability on multicore-distributed memory systems using fast iterative solvers and the Message Passing Interface communication protocol. We demonstrate the computational framework with a shape optimization study of inverted conical frustum pillars. The optimization objective function is the resistance to the CB-W transition, which is quantified by the energy barrier-a relatively large energy barrier suggests improved superhydrophobicity. We also report the parallel performance, in terms of parallel speedup for problems ranging from three hundred thousands to 12 million degrees of freedom, solved using up to 40 processing cores.

Three crystal structures have been analysed from the point of view of intermolecular interactions: N,N'-diphenyl-1,4-benzoquinone diimine, C(18)H(14)N(2), (I), its reduced form N,N'-diphenyl-1,4-phenylenediamine, C(18)H(16)N(2), (II), and N,N'-diphenyl-1,4-phenylenediammonium bis(p-toluenesulfonate), C(18)H(18)N(2)(2+) x 2C(7)H(7)O(3)S(-), (III), which contains fully protonated (II) with p-toluenesulfonic acid. The local molecular C(i) symmetry is preserved in all three structures and the packing seems to be dominated by the mutual arrangement of the simple polyaniline oligomers in the different protonation states. In (I), the most significant molecular interactions are stacking forces, forming columns of molecules along [001]. Close packing of the columns results in C-centering of the structure. In (II), only van der Waals interactions can be observed. In the structure of (III), the p-toluenesulfonate ions serve as acceptors in relatively strong N[bond]H...O hydrogen bonds. The N,N'-diphenyl-1,4-phenylenediammonium cation intercalates between two anions related by a centre of symmetry.

Given the over-parameterization property in recent deep neural networks, sparsification is widely used to compress networks and save memory footprint. Unstructured sparsity, i.e., fine-grained pruning, can help preserve model accuracy, while structured sparsity, i.e., coarse-grained pruning, is preferred for general-purpose hardwares, e.g., GPUs. This paper proposes a novel joint sparsity pattern using mixed granularity to take advantage of both unstructured and structured sparsity. We utilize a heuristic strategy to infer the joint sparsity pattern by mixing vector-wise fine-grained and block-wise coarse-grained pruning masks. Experimental results show that the joint sparsity can achieve higher model accuracy and sparsity ratio while consistently maintaining moderate inference speed for VGG-16 on CIFAR-100 in comparison to the commonly used block sparsity and balanced sparsity strategies.

PSPIKE+: A family of parallel hybrid sparse linear system solvers

1 2 s c 1. Introduction sequential overhead; 2) adding cores often does not result in corresponding speedups: if we get a speedup of 3x with 5 cores, and a speedup of 6x with 10 cores, then the parallel efficiency is only 60%; 3) often the performance plateaus or even degrades after a certain number of cores due to insufficient parallel work, bottlenecks and communication costs. Good parallel scalability is obtained when the sequential overhead is low, the parallel efficiency is high, and bottlenecks and costs that limit the maximum number of cores can be avoided.

Parallel data processing complicates the completion of string similarity joins because parallel data processing requires the use of a well designed data partitioning scheme. Moreover, efficient verification of string pairs is needed to speed up the entire string similarity join process. We propose a novel framework that addresses these requirements through the use of edit distance constraints. The Landmark-Join framework has two functions that reduce two kinds of search spaces. The first, q-bucket partitioning, reduces the number of verifications of dissimilar string pairs and lowers skewness among buckets. The second, local upper bound calculation, prunes the search space of edit distance to speed up each verification. Experimental results show that Landmark-Join has good parallel scalability and that the two proposed functions speed up the entire string similarity join process.