Max size for efficient vectorization?
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As far as I understand, vectorization can highly optimize code, because of its parallelization properties.
As you have for example n separate samples to treat, a classical for loop would imply that the computational time grows linealy: 
.
.By vectorizing, t remains virtually constant as function of n, as all n samples fit together in the shift register of the CPU.
However, this only works as long as 
for some 
that determines the maximum amount of floats to enter the register simultaneously. For 
, computation will be sequential and thus with a linearly growing time again.
for some that determines the maximum amount of floats to enter the register simultaneously. For , computation will be sequential and thus with a linearly growing time again.My question is: is there a simple way to find this ?
?In addition: am I correct that the advantage of using gpuArray is that these can have larger , while c is also larger? Are there similar commands to retrieve these parameters of the GPU?
, while c is also larger? Are there similar commands to retrieve these parameters of the GPU?采纳的回答
Walter Roberson
2019-3-20
编辑：Walter Roberson
2019-3-20
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? The x84 architecture has shift instructions but no shift register.
What the x64 architecture has is cache lines -- groups of bytes that are loaded together even if not explicitly needed by an instruction, under the theory that there is a good chance that the bytes will be needed as well. On most common intel x64 implementations, those cache lines are 64 bytes -- which is merely the size of a double.
Anything beyond that is due to primary cache or secondary cache, which tend to be more model specific.
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Wouter
2019-3-20
Walter Roberson
2019-3-20
For large enough matrices, for a lot of the common operations, MATLAB involves LAPACK or BLAS or MKL (Intel Math Kernel Library), which are high efficiency multi-processor aware routines that know about caches and automatically take cache efficiency into account. Working in blocks (for cache efficiency) can end up using more calculations than the theoretical minimum, but the practical reduction in time can be high. The routines can do a better job of block processing than you could hope to do yourself in MATLAB, so if you have larger arrays it is typically better to have MATLAB take care of the details rather than trying to split the work up yourself. (However if you are using Parallel Computing Toolbox, there is still room for you to use your knowledge of the problem to balance loads more efficiently between workers.)
CUDA is a quite different architecture where what is important is not so much cache but rather that the same instruction is being applied to all locations. Cache does exist, but conditional operation leads unselected processors to be suspended, which can affect processing performance more.
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