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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
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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
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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
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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
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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
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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
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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
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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
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The QPSK codes can be transferred into some existing functions in GPULib: 9 Example
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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
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In implementations of some more operations we will also see the speedup: 11 Example
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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
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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
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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
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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
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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
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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
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The recognition of the operations in QPSK: Add Multiple Divide sin() and cos() 18 Application of CBR to Signal Processing and Software Defined Radio
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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.
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Thank You Comments & Questions? 20
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