初始化一个非标准值的双阵列最快方式

Question

MATLAB用于与像0或1公共值预分配/初始化阵列提供的功能。但是，如果我们想在数组中有一定的任意double价值，也有不同的方法来做到这一点，这不是明显的哪一个是最好。

这个问题是不是新的 - 它的地方，如this blog post和this answer前面所讨论的。然而，经验表明，软件（特别是MATLAB和执行引擎），并随时间改变硬件，所以它可能是最好的办法是在不同的系统不同。不幸的是，上述人士没有提供基准测试代码，这可能是回答这个问题的最终（和永恒）的方式。

我在寻找一个标杆，我可以运行会告诉我最快的方法在我的系统上使用，考虑到我可能会使用不同尺寸的两种“常规” double阵列和阵列gpuArray double，。

Answer 1

function allocationBenchmark(arrSz)
if nargin < 1
  arrSz = 1000;
end

%% RAM
t = [];
disp('--------------- Allocations in RAM ---------------')
t(end+1) = timeit(@()v1(arrSz), 1);
t(end+1) = timeit(@()v2(arrSz), 1);
t(end+1) = timeit(@()v3(arrSz), 1);
t(end+1) = timeit(@()v4(arrSz), 1);
t(end+1) = timeit(@()v5(arrSz), 1);
t(end+1) = timeit(@()v6(arrSz), 1);
t(end+1) = timeit(@()v7(arrSz), 1);
t = 1E3 * t; % conversion to msec
disp(t); disp(" ");
[~,I] = min(t);
disp("Conclusion: method #" + I + " is the fastest on the CPU!"); disp(" ");

%% VRAM
if gpuDeviceCount == 0, return; end
t = [];
disp('--------------- Allocations in VRAM --------------')
t(end+1) = NaN; % impossible (?) to run v1 on the gpu
t(end+1) = gputimeit(@()v2gpu(arrSz), 1);
t(end+1) = gputimeit(@()v3gpu(arrSz), 1);
t(end+1) = gputimeit(@()v4gpu(arrSz), 1);
t(end+1) = gputimeit(@()v5gpu(arrSz), 1);
t(end+1) = gputimeit(@()v6gpu(arrSz), 1);
t(end+1) = gputimeit(@()v7gpu(arrSz), 1);
t = 1E3 * t; % conversion to msec
disp(t); disp(" ");
[~,I] = min(t);
disp("Conclusion: method #" + I + " is the fastest on the GPU!");

end

%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% RAM
function out = v1(M)
% Indexing on the undefined matrix with assignment:
out(1:M, 1:M) = pi;
end

function out = v2(M)
% Indexing on the target value using the `ones` function:
scalar = pi;
out = scalar(ones(M));
end

function out = v3(M)
% Using the `zeros` function with addition:
out = zeros(M, M) + pi;
end

function out = v4(M)
% Using the `repmat` function:
out = repmat(pi, [M, M]);
end

function out = v5(M)
% Using the ones function with multiplication:
out = ones(M) .* pi;
end

function out = v6(M)
% Default initialization with full assignment:
out = zeros(M);
out(:) = pi;
end

function out = v7(M)
% Using the `repelem` function:
out = repelem(pi,M,M);
end

%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% VRAM
function out = v2gpu(M)
scalar = gpuArray(pi);
out = scalar(gpuArray.ones(M));
end

function out = v3gpu(M)
out = gpuArray.zeros(M, M) + gpuArray(pi);
end

function out = v4gpu(M)
out = repmat(gpuArray(pi), [M, M]);
end

function out = v5gpu(M)
out = gpuArray.ones(M) .* gpuArray(pi);
end

function out = v6gpu(M)
% Default initialization with full assignment:
out = gpuArray.zeros(M);
out(:) = gpuArray(pi);
end

function out = v7gpu(M)
% Using the `repelem` function:
out = repelem(gpuArray(pi),M,M);
end

运行以上（例如，用5000的输入）的结果如下：

--------------- Allocations in RAM ---------------
  110.4832  328.1685   48.7895   47.9652  108.8930   93.0481   47.9037

Conclusion: method #7 is the fastest on the CPU!

--------------- Allocations in VRAM --------------
       NaN   37.0322   17.9096   14.2873   17.7377   16.1386   16.6330

Conclusion: method #4 is the fastest on the GPU!

......它告诉我们在任何情况下使用最好的（或同等学历）方法。