1. GPU-Accelerated Computing and Case-Based Reasoning Yanzhi Ren, Jiadi Yu, Yingying Chen Department of Electrical and Computer Engineering, Stevens Institute of Technology, Castle Point on Hudson, Hoboken, NJ, 073030, USA Presenter: Yanzhi Ren September 02, 2010 1

2.  Part I: GPU-Accelerated Computing  GPU and CUDA  GPULib  Application of GPULib  Future work  Part II: Case-Based Reasoning (CBR)  Fundamental of CBR  Application of CBR  Future work 2 Outline

3.  GPUs are massively multithreaded many core chips.  Hundreds of scalar processors.  Tens of thousands of concurrent threads.  Fine-grained data-parallel computation  Users across science & engineering disciplines are achieving tenfold and higher speedups on GPU. 3 GPU

4.  CUDA is the acronym for Compute Unified Device Architecture.  A parallel computing architecture developed by NVIDIA.  CUDA can be accessible to software developers through industry standard programming languages.  CUDA gives developers access to the instruction set and memory of the parallel computation elements in GPUs. 4 CUDA

5.  The CUDA library consists of:  A minimal set of extensions to the C language that allow the programmer to target portions of the source code for execution.  CUDA library includes:  CUBLAS: BLAS implementation  CUFFT: FFT implementation  GPULib: Math implementation 5 CUDA Library

6.  GPULib is built on top of NVIDIA’s Compute Unified Device Architecture (CUDA) platform.  GPULib provides a library of functions that facilitate the use of high performance computing resources.  GPULib provides accelerated computations and high performance computing in technical computing. 6 GPULib

7.  GPULib provides a library of mathematical functions:  Basic functions: addition, subtraction, multiplication, and division, sin(), cos(), gamma(), and exp() and so on.  Other functions: Interpolation, array reshaping, array slicing, and reduction operations. 7 GPULib

8.  Utilize these advantages of the GPULib and replacing some of the existing codes with the functions from the GPULib.  Analyze the operations of the existing codes and then transfer them into the corresponding functions from the GPULib. 8 Application of GPULib

9.  The QPSK codes can be transferred into some existing functions in GPULib: 9 Example

10.  Implementations of common operations such as addition, subtraction, multiplication, and division, sin(), cos(), gamma(), and exp().  We will see five-fold, or even forty-fold, speedup: 10 Example

11.  In implementations of some more operations we will also see the speedup: 11 Example

12.  Consider how to write some simple CUDA codes by utilizing the GPULib for the signal processing on communications, such as BPSK, QPSK and so on.  Try to expand the existing GPULib and write some more useful functions. 12 CUDA: Future Work

13.  Part I: GPU-Accelerated Computing  GPU and CUDA  GPULib  Application of GPULib  Future work  Part II: Case-Based Reasoning (CBR)  Fundamental of CBR  Application of CBR  Future work 13 Outline

14.  By remembering how we solved a similar problem in the past.  Experts often find it easier to relate stories about past cases than to formulate rules.  This is the basic idea of Case Based Reasoning (CBR).  Memory-based problem-solving  Re-using past experiences 14 The Basic Idea of Case Based Reasoning

15.  What is Case-based Reasoning (CBR) ?  CBR is the process of solving new problems based on the solutions of similar past problems.  Medicine  Doctor remembers previous patients especially for rare combinations of symptoms.  Law  Case histories are consulted. 15 Fundamentals of Case Based Reasoning

16.  Distances between values of individual features  Problem and case have values p and c for feature f:  Numeric features:  f(problem,case) = |p - c|/(max difference)  Symbolic features:  f(problem,case)= 0 if p = c = 1 otherwise  Distance is  (problem,case)  Weighted sum of  f(problem,case) for all features  Similarity (problem, case) = 1/(1+  (problem,case)) 16 Similarity Between Problems

17.  Retrieve (Step 1): Given a target problem, retrieve case which is its nearest neighbor from the memory to solving it.  Reuse (Step 2): Map the solution from the previous case to the target problem.  Revise (Step 3): Test the new solution in the real world (or a simulation) and, if necessary, revise.  Retain (Step 4): After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. : 17 Steps of Case Based Reasoning

18.  The recognition of the operations in QPSK:  Add  Multiple  Divide  sin() and cos() 18 Application of CBR to Signal Processing and Software Defined Radio

19. 19 CBR: Future Work  Consider how to use CBR to recognize the features in signal processing primitives.  Consider writing programs which utilize the case based reasoning to recognize the operations in the existing code of Signal Processing and Software Defined Radio.

20. Thank You Comments & Questions? 20