Comparison Table¶. Here is a list of NumPy / SciPy APIs and its corresponding CuPy implementations.-in CuPy column denotes that CuPy implementation is not provided yet.We welcome contributions for these functions.
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- GPU and CUDA Programming. GPU and CUDA examples used during the class; Matrix Multiplication Examples (both using global memory and shared memory) CUDA C Programming Guide; CUDA Toolkit documentation, which includes CUDA installation, C programming guide, APIs for cuBlas, cuFFT etc, tools, compiler SDK, and others.
- Week Two: Memory Model for Locality, Tiling for Conserving Memory Bandwidth, Handling Boundary Conditions, and Performance Considerations, with programming assignment of simple matrix-matrix multiplication in CUDA C. Week Three: Parallel Convolution Pattern, with programming assignment of tiled matrix-matrix multiplication in CUDA C.
ECE408/CS483/CSE408 Spring 2020 Applied Parallel Programming Lecture 4: Memory Model 1 © David Kirk/NVIDIA and Wen-mei W. Hwu, 2007-2018
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We propose the Sliced Coordinate Format (SCOO) for Sparse Matrix-Vector Multiplication on GPUs.An associated CUDA implementation which takes advantage of atomic operations is presented.We propose partitioning methods to transform a given sparse matrix into SCOO format.An efficient Dual-GPU implementation which overlaps computation and ...
- Trying to run a program to do Matrix Multiplication in CUDA. I think I have everything set up correctly and the program runs and executes. Problem is the output. Anyone see whats wrong with my code? Appearently the output matrix has a value of 0 no matter what the inputs are.
(GPU programming) Basic Matrix multiplication in Cuda C (GPU programming) Tiled Matrix Multiplication in CUDA C (GPU programming) Vector Addition in Cuda C; Parallel List Scan in CUDA C; Tricks; Vector Addition with Streams; My Recent Reading Blog; Payment/Authentication in Android. Android Payment by using Braintree; Curl to HTTP POST Request ...
- Matrices used by S. Williams et al for sparse matrix multiplication on GPUs. 14 matrices were used in the following paper: S. Williams, L. Oliker, R. Vuduc, J. Shalf, K. Yelick, J. Demmel, "Optimization of Sparse Matrix-Vector Multiplication on Emerging Multicore Platforms", Parallel Computing Volume 35, Issue 3, March 2009, Pages 178-194.
- Matrix Multiplication (cont.) 21 Optimization NVIDIA GeForce GTX 280 NVIDIA Quadro FX 5600 No optimization 8.8 GBps 0.62 GBps Coalesced using shared memory to store a tile of A 14.3 GBps 7.34 GBps Using shared memory to eliminate redundant reads of a tile of B 29.7 GBps 15.5 GBps
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- Warp Matrix Multiply Add (WMMA) • Warp-wide macro-instructions. • All threads in the warp must be active. Performs matrix multiplication on 16x16 tiles (8x32x16 and 32x8x16 tiles also available) D = A x B + C A and B: FP16 only C and D: Same, either FP16 or FP32. C B A D 16 16 Using Tensor Cores in your CUDA code
Cyclops Tensor Framework (MPI+OpenMP+CUDA) implicit for loops based on index notation (Einstein summation) matrix sums, multiplication, Hadamard product (tensor contractions) distributed symmetric-packed/sparse storage via cyclic layout Jacobi iteration (solves Ax = b iteratively) example code snippet Vector<> Jacobi(Matrix<> A,Vector<> b,intn)