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CUDA Library and Demo Yafeng Yin, Lei Zhou, Hong Man 07/21/2010

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Outline Basic CUDA computation library GPULib, CUBLAS, CUFFT Advanced CUDA computation library CULA /MAGMA, VSIPL CUDA FIR Demo(UMD) Discuss and future work

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Basic lib - GPULib GPULib provides a library of mathematical functions – addition, subtraction, multiplication, and division, as well as unary functions, including sin(), cos(), gamma(), and exp(), – interpolation, array reshaping, array slicing, and reduction operations

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Basic lib - CUBLAS BLAS-- Basic Linear Algebra Subprograms CUBLAS Provide a set of functions for basic vector and matrix operations, such as matrix‐vector copy, sort, dot product, Euclidean norm etc – Real data Level 1 (vector-vector O(N) ) Level 2 (matrix-vector O(N2) ) Level 3 (matrix-matrix O(N3) ) – Complex data Level 1

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CUBLAS-Level 2 function cublasSgbmv()y = alpha * op(A) * x + beta * y cublasSgemv()y = alpha * op(A) * x + beta * y cublasSger()A = alpha * x * yT + A cublasSsbmv()y = alpha * A * x + beta * y, cublasSspmv()y = alpha * A * x + beta * y cublasSspr()A = alpha * x * xT + A cublasSspr2()A = alpha * x * yT + alpha * y * xT + A, cublasSsymv()y = alpha * A * x + beta * y cublasSsyr()A = alpha * x * xT + A cublasSsyr2()A = alpha * x * yT + alpha * y * xT + A, cublasStbmv()x = op(A) * x cublasStbsv()op(A) * x = b, output x

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Basic lib - CUFFT CUFFT is the CUDA FFT library – Provides a simple interface for computing parallel FFT on an NVIDIA GPU – Allows users to leverage the floating-point power and parallelism of the GPU without having to develop a GPU-based FFT implementation – cufftPlan1d(),cufftPlan2d(),cufftPlan3d() Creates a 1D,2D or 3D FFT plan configuration for a specified signal size

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Advanced lib – CULA and MAGMA CULA: GPU Accelerated Linear Algebra – provide LAPACK (Linear Algebra PACKage) function on CUDA GPUs MAGMA: Matrix Algebra on GPU and Multicore Architectures – develop a dense linear algebra library similar to LAPACK but for heterogeneous/hybrid architectures and "Multicore+GPU" systems

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Advanced lib -CULA function Linear Equation Routines – Solves a general system of linear equations AX=B. Orthogonal Factorizations – LQ,RQ factorization Least Squares Routines Symmetric and non- Symmetric Eigenvalue Routines Singular Value Decomposition (SVD) Routines

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Advanced lib - MAGMA LAPACK on CUDA GPUs – LU, QR, and Cholesky factorizations in both real and complex arithmetic (single and double) – Linear solvers based on LU, QR, and Cholesky in real arithmetic (single and double) – Mixed-precision iterative refinement solvers based on LU, QR, and Cholesky in real arithmetic – Reduction to upper Hessenberg form in real arithmetic (single and double) – MAGMA BLAS in real arithmetic (single and double),

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Advanced lib -VSIPL VSIPL: Vector Image Signal Processing Library – Generalized matrix product – Fast FIR filtering – Correlation – Fast Fourier Transform – QR decomposition – Random number generation – Elementwise arithmetic, logical, and comparison operators, linear algebra procedures

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CUDA library Summary Basic vector or matrix computation – GPULib, CUBLAS, CUFFT – vector or matrix: addition, subtraction, multiplication, and division sin(), cos(), sort, dot product, Libraries can be used for Signal Processing – CULA /MAGMA, VSIPL – LU, QR, and Cholesky factorizations – SVD decompostion

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CUDA Demo (FIR) GPU: NVIDIA GeForce 8600 GT CPU: Intel Duo CPU 2.33G Software: Visual Studio 2005

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CUDA Demo (FIR) Output NOGPU Run Time(msec) Memory Time(msec) Total Time CPU + GPU CPU Only Time(msec) 10000.3121210.166641 100000.6672640.284254 1000004.2108701.489784 100000039.4608125.597150 10000000391.81634548.080204

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CUDA Demo (FIR)

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Discuss and future work how to connect CUDA to the SSP re-hosting demo how to change the sequential executed codes in signal processing system to CUDA codes how to transfer the XML codes to CUDA codes to generate the CUDA input.

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Reference CUDA Zone http://www.nvidia.com/object/cuda_home_new.ht ml http://www.nvidia.com/object/cuda_home_new.ht ml http://en.wikipedia.org/wiki/CUDA

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