spscicomp6

Information about spscicomp6

Published on May 2, 2008

Author: Melinda

Source: authorstream.com

Content

Parallel Medical and Genomics Applications on Power3 and Power4 Machines:  Parallel Medical and Genomics Applications on Power3 and Power4 Machines Amit Majumdar San Diego Supercomputer Center - UCSD Application I : Brain Deformation Simulation in Image Guided Neurosurgery Simon K. Warfield1, Florin Talos1,2, Alida Tei1,3, Aditya Bharatha1,4, Arya Nabavi1,2, Matthieu Ferrante1,5, Peter McL. Black2, Ferenc A. Jolesz1, Ron Kikinis1, Corey Kemper1 1Surgical Planing Laboratory and 2Dept. of Surgery Brigham and Women’s Hospital and Harvard Medical School 3Massashusetts Institute of Technology 4University of Toronto Medical School 5Telecom. Lab., Universite’ Catholique de Louvain, Belgium Application II : Monte Carlo SPECT Imaging Yuni Dewaraja1, Kenneth Koral1, Abhijit Bose2, Michael Ljungberg3 1Nuclear Medicine, University of Michigan 2Center for Advanced Computing, University of Michigan 3Department of Radiation Physics, Univ. of Lund, Sweden Application III : Parallel Proteomics Application John R. Yates1, Daniel J. Carucci2 ,Giri Chukkapalli3, Robert Sinkovits3 1The Scripps Research Institute 2Naval Medical Research Center, US NAVY 3SDSC IBM Parallel Machines : compute nodes:  IBM Parallel Machines : compute nodes Power2 and Power3 at CAC University of Michigan : 176 160Mhz Power2; 1Gbytes and 256 Mbytes memory; three 110Mbytes/sec HP switches 3 Power3 nodes, 8 cpu/node, 375 Mhz Power3,8 Gbytes/node; 420 Mbytes/sec Colony switch Power3 at SDSC : 144 nodes with 8zcpu/node, 375 Mhz Power3; 4 Gbytes/node; 350 Mbytes/sec Colony switch Power3 at NAVO MSRC : 334 nodes with 4 cpu/node, 375 Mhs Power4 at TACC University of Texas : 4 Regatta-HPC frames; 16 cpu/node; 1.3 Ghz ; 32 Gbytes/node (can be 64 procs 128 Gbytes memory machine by LL); high-speed dual-plane IBM SP Switch2 Application I : Brain Deformation Simulation in Image Guided Neurosurgery :  Application I : Brain Deformation Simulation in Image Guided Neurosurgery Brain Deformation Simulation in Image Guided Neurosurgery:  Brain Deformation Simulation in Image Guided Neurosurgery Challenge faced by neurosurgeons Remove as much as possible tumor tissue while minimizing removal of healthy tissue Avoid critical anatomical structures Real Time Brain Mapping Enhanced visualization of tumor and critical brain structures Align preoperatively acquired image data with intraoperative images of patient’s brain during surgery Real time constraints The code must meet real-time constraints of neurosurgery – provide images within few minutes few times during surgery lasting few hours Algorithm:  Algorithm Project preoperative image onto intraoperative images Allows fusion of images from multiple imaging modalities and with multiple contrast types Tracks surfaces of key structures in intraoperatively acquired images – allows projection of preoperative images into the patient’s brain configuration during surgery A volumetric deformation field is inferred from the surface changes The field captures nonrigid deformations of the shape of the brain due to brain swelling, cerebrospinal fluid loss, anaesthetic agents and actions of neurosurgeon Current model uses linear elastic material model to represent brain Overall Process:  Overall Process Before Image Guided Neurosurgery : During Image Guided Neurosurgery : Segmentation and Visualization Preoperative Planning of Surgical Trajectory Preoperative Data Acquisition Preoperative data Intraoperative MRI Segmentation Registration Surface matching Solve biomechanical Model for volumetric deformation Visualization Surgical process Volumetric Biomechanical Simulation of Brain Deformation:  Volumetric Biomechanical Simulation of Brain Deformation During surgery brain shape changes due to surgical intervention During surgery surgeon can acquire new volumetric MRI to review current configuration of the entire brain Volumetric Biomechanical Simulation of Brain Deformation Match surface from earlier acquisition to the new acquisition Infer volumetric deformation based upon the surface correspondences Apply forces to the volumetric model that will produce the same displacement field at the surface as was obtained by the surface matching Biomechanical model allows the computation of the deformation throughout the volume Biomechanical Simulation Equations:  Biomechanical Simulation Equations Biomechanical Simulation Equations:  Biomechanical Simulation Equations Mathematical operations, plugging in of interpolation of nodes in terms of linear functions etc. etc. finally gives : Ku = -F K is the stiffness matrix. Displacement at the boundary surface nodes are fixed to match those generated by the active surface model The force vector F is set equal to the displacement vector for the boundary nodes : F = Now solving matrix system for unknown displacement produces deformation field for the entire mesh that matches prescribed displacements at the boundary Signa SP (GE Medical Systems):  Signa SP (GE Medical Systems) R. Pergolizzi Brain shift (1):  Brain shift (1) F. Talos Brain shift (2):  Brain shift (2) F. Talos Linear System Solver:  Linear System Solver The PETSc package is used to solve the linear system Generalized Minimal Residual (GMRES) solver with block Jacobi preconditioning The rows of matrix are divided equally amongst CPUs Global matrix is assembled in parallel Each CPU assembles the local matrix for each element in its subdomain Each CPU has equal # of rows to process Due to irregular connectivity of the meshes some CPUs may do more work than others Performance on Power3 and Power4 Machines:  Performance on Power3 and Power4 Machines Timing Table:  Timing Table Observations:  Observations Power4 timings: Unexpected timings on Power4 16 and 32 processors Scheduler gives exclusive access to nodes and CPUs Cache , network ? Power3 timing is consistent (with other machines) Overall scaling is not good beyond few processors Serial I/O part contributes to this Petsc performance : (GMRES with block jacobi precond) Linear system solver MFLOPS scale well with # of procs on Power3 (have not checked on Power4 yet) Petsc sparse matrix storage allocation is efficient # of GMRES iterations increase with # of processors – 41 to 135 iterations on 1 to 16 procs respectively - contributes partially to scaling Future plan is to investigate scaling further Investigate viscoelastic modelling ($funding$) Thanks to Petsc group (Barry Smith) for valuable discussions Application II : Monte Carlo SPECT Imaging:  Application II : Monte Carlo SPECT Imaging Slide18:  Radionuclide therapy SPECT imaging in radionuclide therapy Monte Carlo simulation of SPECT imaging SIMIND code Applications Parallel Monte Carlo code and performance Radionuclide therapy:  Radionuclide therapy Cancer cells are sterilized using internally administered ionizing radiation Some therapeutic isotopes, ex. I-131, produce both beta particles and gamma ray photons Beta particles kill tumor cells. Beta pathlength span several cells. Photons used to image radioactivity distribution within patient Radionuclide therapy has less toxic effect on normal tissue than chemotherapy. I-131 Radionuclide therapy:  I-131 Radionuclide therapy I-131 Radioimmunotherapy (RIT) : I-131 labeled antibodies selectively target radioactivity to tumor cells while sparing normal tissue. Shows promise for the treatment of non-Hodgkin’s lymphoma (NHL). NHL is the fifth leading cause of cancer death. Median survival 6-10 years. I-131 MIBG Shows promise for the treatment of metastatic neuroblastoma which is a childhood cancer with poor long term survival. I-131 RIT for NHL at University of Michigan:  I-131 RIT for NHL at University of Michigan Phase II clinical trial: Out of 76 patients with no previous treatment, 48 achieved a complete response and 26 achieved a partial response. Patient-specific infusion Tracer dose: ~ 5 mCi for dosimetry studies to determine therapeutic dose for each patient Therapeutic dose: 50 -100 mCi one week later I-131 imaging:  I-131 imaging Single photon emission computed tomography (SPECT) imaging using a rotating gamma camera Components of the gamma camera Lead collimator Detection medium - scintillation crystal Electronics Tomographic reconstruction of SPECT data produces a 3-D image of the radioactivity distribution within the patient. Activity quantification:  Activity quantification For accurate quantification, SPECT data has to be compensated for Patient attenuation Patient scatter Camera response What is the role of M.C. simulation in SPECT Imaging?:  What is the role of M.C. simulation in SPECT Imaging? Monte Carlo is used primarily to evaluate compensation methods for scatter, attenuation and camera response and to evaluate the overall accuracy of our clinical activity quantification. M.C. is ideal for such evaluations because unlike in experiments, the details of photon histories are known. M.C. simulation of SPECT imaging:  M.C. simulation of SPECT imaging SIMIND Monte Carlo code Complete photon transport in phantom and SPECT camera Complex source distributions: analytical or digital phantoms Relatively fast Code has been verified by experiment SIMIND verification using thorax phantom:  SIMIND verification using thorax phantom Measurement with experimental phantom Simulation with byte-coded digital phantom based on CT images SIMIND verification: thorax phantom:  SIMIND verification: thorax phantom M.C. applications: SPECT quantification accuracy using voxel-man phantom:  M.C. applications: SPECT quantification accuracy using voxel-man phantom Clinically realistic case Voxel-man is based on patient CT Realistic activity distribution in organs and tumor Quantification error for large, spherical tumors < 3% Simulation time: 220 hours using 16 SP2 processors (60 projections; 1 billion photon per projection) Work in progress: Monte Carlo generated patient-specific recovery coefficients:  Work in progress: Monte Carlo generated patient-specific recovery coefficients A parallel M.C. code for SPECT: motivation:  A parallel M.C. code for SPECT: motivation Fast code for accurate I-131 Monte Carlo simulations I-131 M.C. simulations are computationally tedious Physical modeling of collimator Variance reduction limited SPECT require a large number of projections Realistic simulations using high resolution voxel phantoms When all of the above are included in a simulation, CPU time can be several months using the serial SIMIND code SIMIND parallelization:  SIMIND parallelization In the present application the photon histories are independent of each other - “inherently parallel” Critical to have a good parallel RNG (SPRNG) Each processor performs entire simulation and reports results to host processor Code replicated in each of the N processors Host sums N partial results and calculates the final result Standard deviation of the combined result is improved by 1/sqrt(N) Minimal changes to original SIMIND code Slide32:  host processor other processors SP2 timing results of one SPECT projection of the voxel-man phantom:  SP2 timing results of one SPECT projection of the voxel-man phantom Power3 timing results of one SPECT projection of the voxel-man phantom:  Power3 timing results of one SPECT projection of the voxel-man phantom Power4 Timing of a different Monte Carlo photon transport code May, 2002 on IBM San Mateo center 32 procs Power4 with large page set ( early access to machine may have contributed to results below):  Power4 Timing of a different Monte Carlo photon transport code May, 2002 on IBM San Mateo center 32 procs Power4 with large page set ( early access to machine may have contributed to results below) More Realistic Simulations:  More Realistic Simulations A 60-projection SPECT simulation of the voxel-man phantom simulation of the voxel-phantom. Realistic values were used for the activity concentration ratios in several organs and tumors (based on typical I-131 RIT patient studies at U. Michigan clinic): kidneys, 81; liver, 26; lungs, 26; spleen 53; blood pool, 48; 100 cc spherical tumor, 100; 50 cc spherical tumor, 100; 20 cc spherical tumor, 100; all other structures, 4. The SPECT matrix size was 64x64x64 with pixel size of 0.8 cm x 0.8 cm x 0.4 cm. Up to 3 orders of scatter was modeled. 1 billion photons were simulated for each projection. The simulation time on Power3 using 512 processors was 6.5 hours for all 60 projections (time for each projection was 6.5 min). Effect of Parallel Computing:  Effect of Parallel Computing Monte Carlo has enabled to evaluate and improve the quantification of I-131 tumor uptake for dosimetry in NHL patients undergoing RIT at U. Michigan clinic. May lead to statistically significant dose-response relationships Speed-up due to the parallel SIMIND code has enabled us to carry out clinically realistic simulations using voxel-phantoms. In the future we will carry out M.C. based dose calculations More accurate Tumor and organ dose distributions Application III : Parallel Proteomics Application:  Application III : Parallel Proteomics Application Slide39:  Sequest is a proteomics application used to analyze mass spectrometer output and match to protein database to identify proteins The Naval Medical Research Center (NMRC) is using Sequest in the research to develop a malaria vaccine based on the expression of proteins in the various stages of the malaria parasite. Performance of the serial Sequest is currently was a major bottleneck in malaria vaccine project SDSC computational scientists developed a parallel version of the Sequest code to reduce simulation time significantly Slide40:  More individuals on the planet with malaria today then ever before in history 300-500 million people become ill with malaria each year 1-3 million children die each year from malaria (200-300 per hour) Drug resistance is spreading rapidly There is no licensed vaccine available anywhere in the world Malaria is a major cause of illness in US troops overseas Facts about Malaria An efficient vaccine should be achievable: Immunity can be acquired naturally Irradiated sporozoites provide > 95% protection Vaccines targeting single proteins were disappointing Current strategy: multistage multicomponent vaccine Slide41:  A Proteomics View of the Malaria Parasite Life Cycle OOne Genome: ~6,000 genes DDifferent Proteomes: Distinct Stages Comprehensively Analyze Protein Complements from 4 P. falciparum Cell Types Identify Stage-specific Targets for Drug and Vaccine Development Importance of a malaria vaccine:  Importance of a malaria vaccine The battle against malaria hampered by the emergence of drug resistant strains of Plasmodium falciparum, the parasite responsible for majority of malaria infections. Most cases of malaria are concentrated in the world's poorest countries. Malaria vaccine likely to be affordable alternative to expensive drugs. Slide43:  Plasmodium falciparum Sporozoites, Trophozoites, Merozoites, Gametocytes Digestion Lysis High-Throughput Proteomics: MudPIT Single Processor Optimization:  Single Processor Optimization ( Timings on 375 MHz IBM Power3 procs.TF:ThermoFinnigan;TSRI:The Scripps Research institute) Code parallelization:  Code parallelization Parallel version of Sequest has been developed using MPI and incorporating all single processor optimizations. Parallelization was done so that all the processors work on a different file simultaneously; files are picked up from a list in round robin distribution by the processors Tests show that good load balancing is achieved by distributing units of work in a round robin fashion. Benchmarks show almost linear scaling on thousands of files Database file (~30 Mb) currently read in once per input file (one test case has ~30,000 input file ; ~1000 procs) Future plan : once per MPI process (reduces I/O from ~ 1 Tb to 30 Gb); eventually database file will be read once by single node Power 3 Timing and Speedup:  Power 3 Timing and Speedup Power3 versus Power4 Speedup on 32 procs :  Power3 versus Power4 Speedup on 32 procs Impact on science:  Impact on science Calculations (anlysis of part of a whole cell lysate of a merozoite sample: late-blood stage in the malaria lifecycle) which would have required 30 days on a single processor now require less than an hour on ~1000 processors of IBM SP Power3 NAVO machine 2x speedup due to single processor tuning observed and ~1000x speedup from parallelization observed Sequest is estimated to be in use at 500 laboratories worldwide – this work impacts entire proteomics community Slide49:  Final Comments Genomics community has already extensively used supercomputing capabilities and will continue to use Now the proteomics community will use supercomputing more and more Medical community is starting to use parallel computing in clinical and operation room procedures such as imaging, modeling of physical organs and their functions etc.

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