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GPU Computation Limitation: Matrix Multiplication vs Vector Addition

This study compares the limitations of GPU and CPU in performing matrix multiplication and vector addition computations. It analyzes data transfer times, computation speeds, and overall efficiency in different scenarios.

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GPU Computation Limitation: Matrix Multiplication vs Vector Addition

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  1. GPU [1] Speaker 高崇閔

  2. Exceed limitation Ecercise2 Exceed limitation Error massage Line 47

  3. CPU V.S. GPU • Question : If we can speed up the computation of CPU, it’s no use about GPU, doesn’t if ? • Reply : In Table II, we spend twice time to transfer data than computation. But, it’s not means we can take place of GPU by CPU. We just transfer data to GPU once, but we can do several times computation in GPU . As a result, the time we cost in transfer is sin that is necessaries.

  4. Thread=512 N=number of block * threads size= size of (float) byteexperimental platform : Geforce 8800 GT Table II Ecercise4

  5. Computation Matrix multiple • A : m*n, B : n*p. A * B : m*p The data we need to transfer is n*(p+m)*sizeof(float)byte The times we do computation is (m*p)*(addition) + (n^2)*(plus) Vector addition • A : 1*n, B : 1*n. A + B : 1*n The data we need to transfer is (2*n)*sizeof(float)byte The times we do computation is (n)*(addition) Ratio of addition and multiple (multiple / addition) The ratio of data transfer is (p+m) The ratio of computation is [(m*p)*(addition) + (n^2)*(plus)] /(n)*(addition)

  6. remain • How to do several data in finite thread • How to computation (multiple) between matrix and vector (inner product and outer product)

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