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

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

3. 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

4. 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

5. CUBLAS-Level 2 function
cublasSgbmv()
y = alpha * op(A) * x + beta * y
cublasSgemv()
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()
cublasSsyr()
cublasSsyr2()
cublasStbmv()
x = op(A) * x
cublasStbsv()
op(A) * x = b , output x

6. 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

7. 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

8. 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

9. 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),

10. 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

11. 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

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

13. CUDA Demo (FIR) Output NO GPU Run Memory Time(msec) Total Time
CPU +GPU
CPU Only
1000
10000
100000

14. CUDA Demo (FIR)

15. 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.

16. Reference CUDA Zone http://www.nvidia.com/object/cuda_home_new.html