Search Results for author:
Found 10 papers, 5 papers with code
Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications
This paper studies the utility of using data analytics and machine learning techniques for identifying, classifying, and characterizing the dynamics of large-scale parallel (MPI) programs.
ECM modeling and performance tuning of SpMV and Lattice QCD on A64FX
We present an architectural analysis of the A64FX used in the Fujitsu FX1000 supercomputer at a level of detail that allows for the construction of Execution-Cache-Memory (ECM) performance models for steady-state loops.
Performance Distributed, Parallel, and Cluster Computing High Energy Physics - Lattice
Analytic Modeling of Idle Waves in Parallel Programs: Communication, Cluster Topology, and Noise Impact
We present a validated analytic model for their propagation velocity with respect to communication parameters and topology, with a special emphasis on sparse communication patterns.
Distributed, Parallel, and Cluster Computing Performance
Multiway $p$-spectral graph cuts on Grassmann manifolds
We demonstrate the effectiveness and accuracy of our algorithm in various artificial test-cases.
A Recursive Algebraic Coloring Technique for Hardware-Efficient Symmetric Sparse Matrix-Vector Multiplication
The symmetric sparse matrix-vector multiplication (SymmSpMV) is an important building block for many numerical linear algebra kernel operations or graph traversal applications.
Distributed, Parallel, and Cluster Computing Performance
Kerncraft: A Tool for Analytic Performance Modeling of Loop Kernels
We then present Kerncraft, a tool that can automatically construct Roofline and ECM models for loop nests by performing the required code, data transfer, and LC analysis.
Performance
GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems
Today, such resources are available as multicore processors, graphics processing units (GPUs), and other accelerators such as the Intel Xeon Phi.
Distributed, Parallel, and Cluster Computing Mathematical Software
Performance Engineering for a Medical Imaging Application on the Intel Xeon Phi Accelerator
We examine the Xeon Phi, which is based on Intel's Many Integrated Cores architecture, for its suitability to run the FDK algorithm--the most commonly used algorithm to perform the 3D image reconstruction in cone-beam computed tomography.
A unified sparse matrix data format for efficient general sparse matrix-vector multiply on modern processors with wide SIMD units
We discuss the advantages of SELL-C-sigma compared to established formats like Compressed Row Storage (CRS) and ELLPACK and show its suitability on a variety of hardware platforms (Intel Sandy Bridge, Intel Xeon Phi and Nvidia Tesla K20) for a wide range of test matrices from different application areas.
Mathematical Software Distributed, Parallel, and Cluster Computing
The Kernel Polynomial Method
Efficient and stable algorithms for the calculation of spectral quantities and correlation functions are some of the key tools in computational condensed matter physics.
Other Condensed Matter Computational Physics
