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© 2009, Interactive Supercomputing, Inc. and its licensors. All rights reserved. Very High Level Languages (VHLL) for No Pain Scalable Computing on High Performance Systems (Linux Clusters, MS HPC 2008 clusters, GPUs, SGI Altix, Cray XT5) Siddharth Samsi, Harrison Ben Smith, Laura Humphrey, Stanley Ahalt, Alan Chalker Ohio Supercomputer Center, Bracy H. Elton, High Performance Technologies, Inc., Roope Astala, Niraj Srivastava, Interactive Supercomputing Inc.
© 2009, Interactive Supercomputing, Inc. and its licensors. All rights reserved. VHHL Overview Use Very High Level Languages MATLAB or Python Star-P runtime handles memory decomposition & management Star-P runtime is abstract HPC resource: SMP or Cluster M language: Python: >> n = 50000*p; >> x = rand(n); >> y = rand(n); >> z = x * y; >> [q r] = qr(x); >>> n = >>> x = starp.numpy.random.rand(n,n) >>> y = starp.numpy.random.rand(n,n) >>> z = starp.numpy.dot(x,y) ◄ x, y distributed objects ◄ qr performed in parallel
© 2009, Interactive Supercomputing, Inc. and its licensors. All rights reserved. Scalability Data parallel computation idx= x = rand(idx,idx*p); y = rand(idx*p,1); tic; z=x\y; toc; Task parallel computation x = rand(1025,1024,idx*p); y = ppeval('fft2filter',x);
© 2009, Interactive Supercomputing, Inc. and its licensors. All rights reserved. Real world Problem Application Radio frequency (RF) tomography imaging Method Illuminate area of interest with transmitting antenna Measure scattered field with receiving antennas Determine reflectivity function from scattered field (“inverse scattering”) Issues Inversion algorithms quite computationally intensive Signal processing algorithms frequently modified & written in Very High Level Languages, e.g., M language of MATLAB® RF tomography sensor systems have mission requirements for timeliness in deployment scenarios Solution Visit our poster for more details!!!
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