Write a OpenMP-OpenCL program to compute vector vector multiplication using global memory model of OpenCL

This is an openMP implementation on host-CPU using p threads of OpenMP and computation on device-GPU. This is an implementation of vector-Vector multiplication using the block striped partitioning algorithm on host-CPU and device-GPU. Each OpenMP thread gets the block of entries of vector and each transfers the block of its vector elements to the device-GPU from host-CPU. On each device-GPU,the partial elements of the two vectors are multipled by calling the OpenCL kernel Vector-VectorMultiplication and writes the partial sclar value. The result scalar is transfered from device-GPU to host-CPU.

Write a OpenMP-OpenCL program to Compute the value of pie value by Numerical Integration using OpenMP directives and OpenCL

This is an openMP implementation on host-CPU using p threads of OpenMP and computation on device-GPU using OpencL APIs One approach is to partition the data among the processes. That is we partition the interval of integration [0,1] among the OpenMP threads, and each thread estimates local integral over its number of subinterval on GPU using OpenCL APIs. The comptuations on GPU produced by the individual OpenMP thread are transformed back to host-CPU . These results are combined on host-CPU to produce the final result. On host-CPU, prints the result.

To perform this integration numerically, divide the interval from 0 to 1 into n subintervals and add up the areas of the rectangles as shown in the Figure 1 (n = 5). Large values of n give more accurate approximations of pi . Use MPI point-to-point communication library calls.

We assume that n is total number of subintervals, p is the number of processes and p < n. One simple way to distribute the total number of subintervals to each process is to dividen by p. There are two kinds of mappings that balance the load. One is a block mapping, partitions the array elements into blocks of consecutive entries and assigns the block to the processes. The other mapping is a cyclic mapping. It assigns the first element to the first process, the second element to the second, and so on. If n > p, we get back to the first process, and repeat the assignment process for remaining elements. This process is repeated until all the elements are assigned. We have used a cyclic mapping for partition of interval [0,1] onto p processes.

Write a OpenMP-OpenCL program to Matrix vector multiplication based on CUBLAS1 library funcation calls to compute vector vector multiplication

This is an openMP implementation on host-CPU using p threads of OpenMP and computation on device-GPU. This is an implementation of Matrix-Vector multiplication using the block striped partitioning algorithm on host-CPU and device-GPU. Each OpenMP thread gets the block of rows of the matrix and each transfers the block of its rows and vector to the device-GPU from host-CPU. Then each device-GPU multiplies the corresponding block matrix with the vector by calling the OpenCL kernel VectorVectorMultiplication (BLAS1 library call) and writes the partial product into the result vector. The result vector is transfered from device-GPU to host-CPU. on host-CPU to the output array.

Write a OpenMP OpenCL program to compute Matrix Matrix multiplication based on OpenMP & OpenCL directives and use BLAS1 library funcation calls to compute vector vector multiplication

This is an openMP implementation Matrix-Matrix multiplication on host-CPU using p threads of OpenMP and computation on device-GPU in which block striped partitioning of the both input matrices on host-CPU is performance and on device-GPU, the CUDA BLAS1 library call is used to compute vector-vector multiplication algorithm. Each OpenMP thread gets the block of rows of the input matrix A and a block of columns of input matrix B. Computations of block matrices are performed on device-GPU using tuned BLAS1 mathematical libraries on device-GPU. Then, one device-GPU multiplies the corresponding block matrices by calling the OpenCL kernel MatrixMatrixMultiplication using BLAS1 library and writes the partial output matrix i.e., C . The result block matrix C is transfered from device-GPU to host-CPU.

Write a OpenMP OpenCL program to compute Matrix Matrix multiplication based on OpenMP & OpenCL directives using global and local memory

This is an openMP implementation Matrix-Matrix multiplication on host-CPU using p threads of OpenMP and computation on device-GPU in which block striped partitioning of the both input matrices on host-CPU is performed and on device-GPU. Each OpenMP thread gets the block of rows of the input matrix A and a block of columns of input matrix B. Computations of block matrices are performed on device-GPU on device-GPU. Then, one device-GPU multiplies the corresponding block matrices by calling the OpenCL kernel performs MatrixMatrixMultiplication and writes the partial output matrix i.e., C . The result block matrix C is transfered from device-GPU to host-CPU.