Test for arm64 & x86 runtime performance

[adeeconometrics]

Should see if the refactored Matric::operator= improve performance for larger matrices

[adeeconometrics]

dev-lazymatrix implements a simpler std::vector internal, some of the benefits found in this branch are:

• Better compiler optimization for clang loop vectorize up to 690 GFLOPs for M2
• Allows single standard Matrix implementation, some contentions in reverting back to std::array for smaller matrices for $\mathbb{R}^{M \times N} < \mathbb{R}^{256 \times 256}$ are: (a) stack size varies per hardware per type so it is difficult to design for reliable performance and universal types, (b) C++ standard does not impose stack size requirement for std::thread library so it's difficult to conditionally adapt $M,N$ size per hardware (metadata is not available).

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Note: Function that was evaluated $$A \times B + A \cdot B \cdot (\sin(A) \times \cos(A) + B)$$

[adeeconometrics]

Avenues to explore:

• [ ] GCC equivalence of loop vectorized directive and associated compiler optimization for SIMD
• [ ] See if it is worth it to specialize for NEON/SSE intrinsic for special types