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Point operations are nonlinear operations, which include contrast stretching, histogram modification, and pseudo-coloring.
Sub-word logical and integer operations as well as fixed point operations are also possible.
All of the floating point operations are single precision (32-bit) floating point operations.
As floating point operations are much more complex than integer-based operations, the complexity of the computation hardware also increases.
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One of the most important nonlinear point operations is histogram equalization, also called histogram flattening.
The optimal number of floating point operations is architecturally invariant so the relative performance is determined by communication time.
For simplicity, the number of float point operations is used to measure the computational complexity of the proposed algorithms.
In addition, floating point operations were divided into single and double precision, and totaled separately for the CPU and GPU.
The total number of complex floating point operations is approximately sumlimits_{k=1}^{K}sumlimits_{n=1}^{N},8nkleft[8L_{R}left {ML}_{T}right)^{2} + 9left {ML}_{T}right)^{3}right].
Techniques for shared memory communication, such as message segmentation and collectives, not based on point-to-point operations, are substantively different from their inter-node counterparts.
The routines that realize this type of two-sided orthogonal reductions (TSOR) in LAPACK are especially challenging, since a significant fraction of their floating-point operations are cast in terms of memory-bound kernels while the remaining part corresponds to efficient compute-bound kernels.
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