Test-driving Intel Xeon Phi

Fang, Jianbin; Sips, Henk; Zhang, Lilun; Xu, Chuanfu; Che, Yue; Vărbănescu, Ana Lucia

doi:10.1145/2568088.2576799

Cited by 73 publications

(52 citation statements)

References 17 publications

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“…The plot recipes use the solver object to build a default plot which is customizable using any of the commands from the Plots.jl package, and can be plotted to any plotting backend provided by Plots.jl. For example, we can by default plot to the PyPlot.jl 5 backend (a Julia wrapper for matplotlib 6 ) via the command:…”

Section: Solving Odesmentioning

confidence: 99%

See 1 more Smart Citation

DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia

Rackauckas

Nie

2017

JORS

1,067

809

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DifferentialEquations.jl is a package for solving differential equations in Julia. It covers discrete equations (function maps, discrete stochastic (Gillespie/Markov) simulations), ordinary differential equations, stochastic differential equations, algebraic differential equations, delay differential equations, hybrid differential equations, jump diffusions, and (stochastic) partial differential equations. Through extensive use of multiple dispatch, metaprogramming, plot recipes, foreign function interfaces (FFI), and call-overloading, DifferentialEquations.jl offers a unified user interface to solve and analyze various forms of differential equations while not sacrificing features or performance. Many modern features are integrated into the solvers, such as allowing arbitrary user-defined number systems for high-precision and arithmetic with physical units, built-in multithreading and parallelism, and symbolic calculation of Jacobians. Integrated into the package is an algorithm testing and benchmarking suite to both ensure accuracy and serve as an easy way for researchers to develop and distribute their own methods. Together, these features build a highly extendable suite which is feature-rich and highly performant.

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Section: Solving Odesmentioning

confidence: 99%

“…Also, when many of these methods were implemented the standard computer was limited by the speed of the processor. These days, most processors are multi-core and many computers contain GPGPU [1] or Xeon Phi [17,6] acceleration cards and thus taking advantage of the ever-present parallelism is key to achieving good performance.…”

Section: Introductionmentioning

confidence: 99%

DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia

Rackauckas

Nie

2017

JORS

1,067

809

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“…Intel Xeon Phi (MIC) cards are connected to the host processor through the PCIe 2 Based on the saxpy1 benchmark in [34].…”

Section: Computing Platformsmentioning

confidence: 99%

A High Performance Block Eigensolver for Nuclear Configuration Interaction Calculations

Aktulga

Afibuzzaman

Williams

et al. 2017

IEEE Trans. Parallel Distrib. Syst.

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Abstract-As on-node parallelism increases and the performance gap between the processor and the memory system widens, achieving high performance in large-scale scientific applications requires an architecture-aware design of algorithms and solvers. We focus on the eigenvalue problem arising in nuclear Configuration Interaction (CI) calculations, where a few extreme eigenpairs of a sparse symmetric matrix are needed. We consider a block iterative eigensolver whose main computational kernels are the multiplication of a sparse matrix with multiple vectors (SpMM), and tall-skinny matrix operations. We present techniques to significantly improve the SpMM and the transpose operation SpMM T by using the compressed sparse blocks (CSB) format. We achieve 3-4× speedup on the requisite operations over good implementations with the commonly used compressed sparse row (CSR) format. We develop a performance model that allows us to correctly estimate the performance of our SpMM kernel implementations, and we identify cache bandwidth as a potential performance bottleneck beyond DRAM. We also analyze and optimize the performance of LOBPCG kernels (inner product and linear combinations on multiple vectors) and show up to 15× speedup over using high performance BLAS libraries for these operations. The resulting high performance LOBPCG solver achieves 1.4× to 1.8× speedup over the existing Lanczos solver on a series of CI computations on high-end multicore architectures (Intel Xeons). We also analyze the performance of our techniques on an Intel Xeon Phi Knights Corner (KNC) processor.

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“…The profiling data shows that data marshaling kernels do not utilize the vector units on Intel MIC efficiently, due to the non-unit stride memory access pattern and complex control flow. In addition, the hardware prefetcher in Intel MIC is less powerful than mainstream multicore CPUs, when the memory access stream is non-contiguous (scattered) [14]. Figure 10 shows the performance of the 3D dotproduct benchmark, when accelerated using MetaMorph with platform-specific BLAS libraries.…”

Section: Resultsmentioning

confidence: 99%

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-Core Clusters

Helal¹,

Tech²,

Sathre³

et al. 2016

SC16: International Conference for High Performance Computing, Networking, Storage and Analysis

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Abstract-To attain scalable performance efficiently, the HPC community expects future exascale systems to consist of multiple nodes, each with different types of hardware accelerators. In addition to GPUs and Intel MICs, additional candidate accelerators include embedded multiprocessors and FPGAs. End users need appropriate tools to efficiently use the available compute resources in such systems, both within a compute node and across compute nodes. As such, we present MetaMorph, a library framework designed to (automatically) extract as much computational capability as possible from HPC systems. Its design centers around three core principles: abstraction, interoperability, and adaptivity. To demonstrate its efficacy, we present a case study that uses the structured grids design pattern, which is heavily used in computational fluid dynamics. We show how MetaMorph significantly reduces the development time, while delivering performance and interoperability across an array of heterogeneous devices, including multicore CPUs, Intel MICs, AMD GPUs, and NVIDIA GPUs.

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Test-driving Intel Xeon Phi

Cited by 73 publications

References 17 publications

DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia

DifferentialEquations.jl – A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia

A High Performance Block Eigensolver for Nuclear Configuration Interaction Calculations

MetaMorph: A Library Framework for Interoperable Kernels on Multi- and Many-Core Clusters

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