1 Matrix Multiplication

In the lecture and discussion, we discussed two approaches to compute matrix multiplication (C = AB) using CUDA: (1) unoptimized implementation using global memory only and (2) block matrix multiplication using shared memory.

In this assignment, your task is implementing 1024 1024 matrix multiplication using these two approaches.

• Approach 1 (unoptimized implementation using global memory only):
  • Name this program as p1.cu
  • The value of each element of A is 1
  • The value of each element of B is 2
  • Thread block configuration: 16 16
  • Grid configuration: 64 64
  • After computation, print the value of C[451][451]
• Approach 2 (block matrix multiplication using shared memory):
  • Name this program as p2.cu
  • The value of each element of A is 1
  • The value of each element of B is 2
  • Thread block configuration: 32 32
  • Grid configuration: 32 32
  • More details of this algorithm can be found in the paper Matrix Multiplication with CUDA under the Readings category of blackboard.
  • After computation, print the value of C[451][451]
• Report: measure the execution time of the kernel of Approach 1 and Approach 2, respectively. Briefly discuss your observations.

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