Assessing the role of mini-applications in predicting key performance characteristics of scientific and engineering applications

Computational science and engineering application programs are typically large, complex, and dynamic, and are often constrained by distribution limitations. As a means of making tractable rapid explorations of scientific and engineering application programs in the context of new, emerging, and future computing architectures, a suite of "miniapps" has been created to serve as proxies for full scale applications. Each miniapp is designed to represent a key performance characteristic that does or is expected to significantly impact the runtime performance of an application program. In this paper we introduce a methodology for assessing the ability of these miniapps to effectively represent these performance issues. We applied this methodology to three miniapps, examining the linkage between them and an application they are intended to represent. Herein we evaluate the fidelity of that linkage. This work represents the initial steps required to begin to answer the question, "Under what conditions does a miniapp represent a key performance characteristic in a full app?

A fast sparse block circulant matrix vector product

In the context of computed tomography (CT), iterative image reconstruction techniques are gaining attention because high-quality images are becoming computationally feasible. They involve the solution of large systems of equations, whose cost is dominated by the sparse matrix vector product (SpMV). Our work considers the case of the sparse matrices being block circulant, which arises when taking advantage of the rotational symmetry in the tomographic system. Besides the straightforward storage saving, we exploit the circulant structure to rewrite the poor-performance SpMVs into a high-performance product between sparse and dense matrices. This paper describes the implementations developed for multi-core CPUs and GPUs, and presents experimental results with typical CT matrices. The presented approach is up to ten times faster than without exploiting the circulant structure.Romero Alcalde, E.; Tomás Domínguez, AE.; Soriano Asensi, A.; Blanquer Espert, I. (2014). A fast sparse block circulant matrix vector product. En Euro-Par 2014 Parallel Processing. Springer. 548-559. doi:10.1007/978-3-319-09873-9_46S548559Bian, J., Siewerdsen, J.H., Han, X., Sidky, E.Y., Prince, J.L., Pelizzari, C.A., Pal, X.: Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam ct. Physics in Medicine and Biology 55, 6575–6599 (2010)Dalton, S., Bell, N.: CUSP: A C++ templated sparse matrix library version 0.4.0 (2014), http://cusplibrary.github.com/Feldkamp, L., Davis, L., Kress, J.: Practical cone-beam algorithm. Journal of the Optical Society of America 1, 612–619 (1984)Ganine, V., Legrand, M., Michalska, H., Pierre, C.: A sparse preconditioned iterative method for vibration analysis of geometrically mistuned bladed disks. Computers & Structures 87(5-6), 342–354 (2009)Hara, A.K., Paden, R.G., Silva, A.C., Kujak, J.L., Lawder, H.J., Pavlicek, W.: Iterative reconstruction technique for reducing body radiation dose at CT: Feasibility study. American Journal of Roentgenology 193, 764–771 (2009)Heroux, M.A., Bartlett, R.A., Howle, V.E., Hoekstra, R.J., Hu, J.J., Kolda, T.G., Lehoucq, R.B., Long, K.R., Pawlowski, R.P., Phipps, E.T., Salinger, A.G., Thornquist, H.K., Tuminaro, R.S., Willenbring, J.M., Williams, A., Stanley, K.S.: An overview of the Trilinos project. ACM Trans. Math. Softw. 31(3), 397–423 (2005)Im, E.J., Yelick, K., Vuduc, R.: Sparsity: Optimization framework for sparse matrix kernels. International Journal of High Performance Computing Applications 18(1), 135–158 (2004)Jones, E., Oliphant, T., Peterson, P., et al.: SciPy: Open source scientific tools for Python (2001), http://www.scipy.org/Kaveh, A., Rahami, H.: Block circulant matrices and applications in free vibration analysis of cyclically repetitive structures. Acta Mechanica 217(1-2), 51–62 (2011)Kourtis, K., Goumas, G., Koziris, N.: Optimizing sparse matrix-vector multiplication using index and value compression. In: Proceedings of the 5th Conference on Computing Frontiers, CF 2008, pp. 87–96. ACM, New York (2008)Krotkiewski, M., Dabrowski, M.: Parallel symmetric sparse matrix–vector product on scalar multi-core CPUs. Parallel Computing 36(4), 181–198 (2010)Lee, B., Vuduc, R., Demmel, J., Yelick, K.: Performance models for evaluation and automatic tuning of symmetric sparse matrix-vector multiply. In: International Conference on Parallel Processing, ICPP 2004, vol. 1, pp. 169–176 (2004)Leroux, J.D., Selivanov, V., Fontaine, R., Lecomte, R.: Accelerated iterative image reconstruction methods based on block-circulant system matrix derived from a cylindrical image representation. In: Nuclear Science Symposium Conference Record, NSS 2007, vol. 4, pp. 2764–2771. IEEE (2007)NVIDIA: CUSPARSE library (2014), https://developer.nvidia.com/cusparsePan, X., Sidky, E.Y., Vannier, M.: Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? Inverse Problems 25, 123009 (2008)Rodríguez-Alvarez, M.J., Soriano, A., Iborra, A., Sánchez, F., González, A.J., Conde, P., Hernández, L., Moliner, L., Orero, A., Vidal, L.F., Benlloch, J.M.: Expectation maximization (EM) algorithms using polar symmetries for computed tomography CT image reconstruction. Computers in Biology and Medicine 43(8), 1053–1061 (2013)Sheep, L., Vardi, Y.: Maximum likelihood reconstruction for emmision tomography. IEEE Transactions on Medical Imaging 1, 113–122 (1982)Sidky, E.Y., Pan, X.: Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization. Physics in Medicine and Biology 53, 4777–4807 (2008)Soriano, A., Rodríguez-Alvarez, M.J., Iborra, A., Sánchez, F., Carles, M., Conde, P., González, A.J., Hernández, L., Moliner, L., Orero, A., Vidal, L.F., Benlloch, J.M.: EM tomographic image reconstruction using polar voxels. Journal of Instrumentation 8, C01004 (2013)Thibaudeau, C., Leroux, J.D., Pratte, J.F., Fontaine, R., Lecomte, R.: Cylindrical and spherical ray-tracing for ct iterative reconstruction. In: 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), pp. 4378–4381 (2011)Vuduc, R., Demmel, J.W., Yelick, K.A.: OSKI: A library of automatically tuned sparse matrix kernels. Journal of Physics: Conference Series 16(1), 521 (2005)Vuduc, R.W., Moon, H.-J.: Fast sparse matrix-vector multiplication by exploiting variable block structure. In: Yang, L.T., Rana, O.F., Di Martino, B., Dongarra, J. (eds.) HPCC 2005. LNCS, vol. 3726, pp. 807–816. Springer, Heidelberg (2005)Williams, S., Oliker, L., Vuduc, R., Shalf, J., Yelick, K., Demmel, J.: Optimization of sparse matrix-vector multiplication on emerging multicore platforms. Parallel Computing 35(3), 178–194 (2009

Tpetra, and the Use of Generic Programming in Scientific Computing

We present Tpetra, a Trilinos package for parallel linear algebra primitives implementing the Petra object model. We describe Tpetra's design, based on generic programming via C++ templated types and template metaprogramming. We discuss some benefits of this approach in the context of scientific computing, with illustrations consisting of code and notable empirical results

Towards an Exascale Enabled Sparse Solver Repository

As we approach the Exascale computing era, disruptive changes in the software landscape are required to tackle the challenges posed by manycore CPUs and accelerators. We discuss the development of a new `Exascale enabled' sparse solver repository (the ESSR) that addresses these challenges---from fundamental design considerations and development processes to actual implementations of some prototypical iterative schemes for computing eigenvalues of sparse matrices. Key features of the ESSR include holistic performance engineering, tight integration between software layers and mechanisms to mitigate hardware failures

The sparse BLAS

SIGLEAvailable from British Library Document Supply Centre-DSC:8715.1804(2001/032) / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Cooperative Application/OS DRAM Fault Recovery

Exascale systems will present considerable fault-tolerance challenges to applications and system software. These systems are expected to suffer several hard and soft errors per day. Unfortunately, many fault-tolerance methods in use, such as rollback recovery, are unsuitable for many expected errors, for example DRAM failures. As a result, applications will need to address these resilience challenges to more effectively utilize future systems. In this paper, we describe work on a cross-layer application/OS framework to handle uncorrected memory errors. We illustrate the use of this framework through its integration with a new fault-tolerant iterative solver within the Trilinos library, and present initial convergence results