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CS267 Class Project Suggestions Spring 2012

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Class project suggestions Many kinds of projects – Reflects broad scope of field and of students, from many departments Need to do one or more of design / program / measure some parallel application / kernel / software tool / hardware Can work alone or in teams – HW0 posted to help identify possible teammates based on interest What you need to do – Project proposal by midnight Saturday Mar 24 – Feedback from instructor over spring break (ongoing conversations) – Poster presentation (+ recording short video presentation) on Thursday May 3 (class time, during RRR week) – Final report writeups due Monday May 7 at midnight 3/22/12CS267 Class Projects2

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How to Organize A Project Proposal (1/2) Parallelizing/comparing implementations of an Application Parallelizing/comparing implementations of a Kernel Building /evaluating a parallel software tool Evaluating parallel hardware 3/22/12CS267 Class Projects3

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How to Organize A Project Proposal (2/2) What is the list of tasks you will try? – Sorted from low-hanging fruit to harder What existing tools you will use, compare to? – Don’t reinvent wheels, or at least compare to existing wheels to see if yours is better – For applications, consider using frameworks like Chombo or PETSC Lecture April 10, in meantime see Lecture 23 from CS267 Spr11 – For applications, identify computational and structural patterns you plan to use What are your success metrics – Get application X up on Hopper, solve problem Y – Get motif Z to run W times faster on GPU – Collect data V to evaluate/compare approaches 3/22/12CS267 Class Projects4

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A few sample CS267 Class Projects all posters and video presentations at www.cs.berkeley.edu/~demmel/cs267_Spr09/posters.html Content based image recognition – “Find me other pictures of the person in this picture” Faster molecular dynamics, applied to Alzheimer’s Disease Better speech recognition through a faster “inference engine” Faster algorithms to tolerate errors in new genome sequencers Faster simulation of marine zooplankton population Sharing cell-phone bandwidth for faster transfers 3/22/12CS267 Class Projects5

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More Prior Projects 1.High-Throughput, Accurate Image Contour DetectionHigh-Throughput, Accurate Image Contour Detection 2.CUDA-based rendering of 3D Minkowski SumsCUDA-based rendering of 3D Minkowski Sums 3.Parallel Particle FiltersParallel Particle Filters 4.Scaling Content Based Image Retrieval SystemsScaling Content Based Image Retrieval Systems 5.Towards a parallel implementation of the Growing String MethodTowards a parallel implementation of the Growing String Method 6.Optimization of the Poisson Operator in CHOMBOOptimization of the Poisson Operator in CHOMBO 7.Sparse-Matrix-Vector-Multiplication on GPUsSparse-Matrix-Vector-Multiplication on GPUs 8.Parallel RI-MP2Parallel RI-MP2 3/22/12CS267 Class Projects6

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More Prior Projects 1.Parallel FFTs in 3D: Testing different implementation schemesParallel FFTs in 3D: Testing different implementation schemes 2.Replica Exchange Molecular Dynamics (REMD) for Amber's Particle- Mesh Ewald MD (PMEMD)Replica Exchange Molecular Dynamics (REMD) for Amber's Particle- Mesh Ewald MD (PMEMD) 3.Creating a Scalable HMM based Inference Engine for Large Vocabulary Continuous Speech RecognitionCreating a Scalable HMM based Inference Engine for Large Vocabulary Continuous Speech Recognition 4.Using exponential integrators to solve large stiff problemUsing exponential integrators to solve large stiff problem 5.Clustering overlapping reads without using a reference genomeClustering overlapping reads without using a reference genome 6.An AggreGATE Network Abstraction for Mobile DevicesAn AggreGATE Network Abstraction for Mobile Devices 7.Parallel implementation of multipole-based Poisson-Boltzmann solverParallel implementation of multipole-based Poisson-Boltzmann solver 8.Finite Element Simulation of Nonlinear Elastic Dynamics using CUDAFinite Element Simulation of Nonlinear Elastic Dynamics using CUDA 3/22/12CS267 Class Projects7

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Still more prior projects 1.Parallel Groebner Basis Computation using GASNetParallel Groebner Basis Computation using GASNet 2.Accelerating Mesoscale Molecular Simulation using CUDA and MPIAccelerating Mesoscale Molecular Simulation using CUDA and MPI 3.Modeling and simulation of red blood cell light scatteringModeling and simulation of red blood cell light scattering 4.NURBS Evaluation and RenderingNURBS Evaluation and Rendering 5.Performance Variability in Hadoop's Map ReducePerformance Variability in Hadoop's Map Reduce 6.Utilizing Multiple Virtual Machines in Legacy Desktop ApplicationsUtilizing Multiple Virtual Machines in Legacy Desktop Applications 7.How Useful are Performance Counters, Really? Profiling Chombo Finite Methods Solver and Parsec Fluids Codes on Nehalem and SiCortexHow Useful are Performance Counters, Really? Profiling Chombo Finite Methods Solver and Parsec Fluids Codes on Nehalem and SiCortex 8.Energy Efficiency of MapReduceEnergy Efficiency of MapReduce 9.Symmetric Eigenvalue Problem: Reduction to TridiagonalSymmetric Eigenvalue Problem: Reduction to Tridiagonal 10.Parallel POPCycle ImplementationParallel POPCycle Implementation 3/22/12CS267 Class Projects 8

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Class Project Suggestions (1/7) Pick one (of many) functions from one of the 13 motifs Pick a target parallel platform Pick a “parallel programming framework,” eg for dense linear algebra –LAPACK – all parallelism in BLAS –ScaLAPACK – distributed memory using MPI –PLASMA – DAG scheduling on multicore Parallel Linear Algebra for Scalable Multi-core Architectures http://icl.cs.utk.edu/plasma/ –MAGMA – DAG scheduling for heterogeneous platforms Matrix Algebra on GPU and Multicore Architectures http://icl.cs.utk.edu/magma/ –Cloud –FLAME - http://z.cs.utexas.edu/wiki/flame.wiki/FrontPagehttp://z.cs.utexas.edu/wiki/flame.wiki/FrontPage Design, implement, measure, model and/or compare performance –Can be missing entirely on target platform –May exist, but with a different programming framework 3/22/12CS267 Class Projects 9

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Class Project Suggestions (2/7) 3/22/12CS267 Class Projects °Many new algorithmic ideas for sparse linear algebra °Come to BEBOP meetings (T 12:30 – 2, 380 Soda) °Experiment with SpMV on different architectures Which optimizations are most effective? °Try to speed up particular matrices of interest Data mining, “bottom solver” from AMR °Explore tuning space [x,Ax,…,A k x] kernel Different matrix representations (last slide) New Krylov subspace methods, preconditioning °Experiment with new frameworks (SPF) 10

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Class Project Suggestions (3/7) Proposed by Sherry Li, LBL Staff Scientist “Feasibility of Communication-Avoiding Panel Factorization in Sparse LU” Based on SuperLU – widely used parallel sparse LU factorization routine – Bottleneck: factorization of “small” panel at each step – Project (1): instrument code to evaluate potential communication bottleneck, potential for speedup by using “Tall-Skinny LU” (TSLU – Project (2): implement, insert TSLU 3/22/12CS267 Class Projects11

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Class Project Suggestions (4/7) Proposed by Oded Schwartz, ParLab postdoc “Automatic parallelization of BFS/DFS algorithms using SEJITS” Motivated by common pattern in several optimal D&C algorithms for matrix multiplication – Traverse D&C tree by BFS until out of memory for replicating data, then switch to DFS Use SEJITS to parallelize Python code exploiting this pattern, apply to various algorithms 3/22/12CS267 Class Projects12

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Class Project Suggestions (5/7) Proposed by Oded Schwartz, ParLab postdoc Variety of fast linear algebra algorithms to be paralleized – Yuster-Zwick algorithm for sparse matrix multiplication – Variety of fast algorithms beyond Strassen: which ones are fast? Parallelizable as well as Strassen? 3/22/12CS267 Class Projects13

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Class Project Suggestions (6/7) Proposed by Derrick Coetzee, ParLab grad student “Communication and arithmetic optimal long integer arithmetic” Derrick found an implementation of the Shonhage-Strassen integer multiply algorithm that minimizes communication, and would like to collaborate on implementing, measuring it 3/22/12CS267 Class Projects14

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Class Project Suggestions (7/7) “Minimizing the energy of a computation” Energy required to perform computation (on a handheld device or supercomputer) is becoming the bottleneck Communication (moving data) takes much more energy than arithmetic How well do our communication-avoiding algorithms minimize energy? 3/22/12CS267 Class Projects15

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