[SOLVED] Ecse 4320/6320 course project #2: matrix-matrix multiplication with simd instructions & cache miss minimization

1. Introduction
The objective of this design project is to implement a C/C++ module that carries out high-speed matrix-matrix
multiplication by explicitly utilizing x86 SIMD instructions and minimizing cache miss rate via restructuring data
access patterns. Matrix-matrix multiplication is one of the most important data processing kernels in numerous
real-life applications, e.g., machine learning, computer vision, signal processing, and scientific computing. This
project aims to help you gain hands-on experience of (1) SIMD programming, and (2) cache access optimization. It
will help you develop a deeper understanding of the importance of exploiting data-level parallelism and minimizing
cache miss rate.
2. Requirement
Your implementation should be able to support (1) configurable matrix size that can be much larger than the onchip cache capacity, and (2) both fixed-point and floating-point data. Each group should create a Github site that
hosts the code/results of all the projects through this semester. Other than the source code, your Github site should
contain
(1) Readme that clearly explains the structure/installation/usage of your code
(2) Experimental results that show the performance of your code under different matrix size (at least including
1,000×1,000 and 10,000×10,000) and different data precision (4-byte floating-point, 2-byte fixed-point)
(3) Comparison with native implementation of matrix-matrix multiplication (i.e., without any cache
optimization and explicit use of SIMD instructions)
(4) Analysis and conclusion
3. Additional Information
The easiest way to use SIMD instructions is to call the intrinsic functions in your C/C++ code. The complete reference
of the intrinsic functions can be found at https://software.intel.com/sites/landingpage/IntrinsicsGuide/, and you
can find many on-line materials about their usage. Moreover, matrix-matrix multiplication has been well studied in
the industry, and one well-known library is the Intel Math Kernel Library (MKL), which can be a good reference for
you.