LoopVectorization: 高效的 for 循环优化 LoopVectorization 通过对 Julia 的 for 循环 IR 表达式进行开销分析来推断最优的 for 循环展开模式,从而实现了非常高效的代码生成。 下图是 LoopVectorization 对矩阵乘法实现进行优化后的实现,在这里 @turbo 可以看到,LoopVectoriza
LoopVectorization 通过对 Julia 的 for 循环 IR 表达式进行开销分析来推断最优的 for 循环展开模式,从而实现了非常高效的代码生成。 下图是 LoopVectorization 对矩阵乘法实现进行优化后的实现,在这里 @turbo 可以看到,LoopVectorization 取得了与 MKL 接近的性能。 自从去年 LoopVectorization 发布以后,就收获了大量的社...
Python+Numba implementation with parallel computation 接下来我们对上面的代码做一点点修改,加进parallel和prange,看看能不能起到什么作用。 importnumpyasnpimportnumbafromtimeitimportdefault_timerastimer@numba.njit(parallel=True)defcalc_pi(nMC):radius=1.diameter=2.*radiusn_circle=0foriinnumba.prange(nMC)...
The support for multithreading programming in Julia was only released last year, and therefore still requires performance studies. In this work, we focus on the parallel loops and more specifically on the available mechanisms for assigning the loop iterations to the threads. We analyse the per-...
functionmain()...some common code...fortime=1:Nfunctionfun1()#Iwantthisfunctionto run parallel...functionfun2()#..thisfunctionto run parallelwith1,3,4functionfun3()#..Thisfunctionto run parallelwith2,3,4functionfun4()#..Thisfunctionto run parallelwith1,2,3end...more code here...retu...
在CUDA.jl 提供了直接编译核函数到 CUDA 设备上的能力之后,今年出现了一些围绕这一点展开的高级封装,例如:KernelAbstractions 和 ParallelStencil。他们提供了将手写的核函数根据需要编译到 CPU、GPU 等异构设备上的能力,从而避免了对不同计算设备写多个核函数的需要。
Converting from a ForEach loop to a Parallel.ForEach loop when summarizing into a double slows things down I have a section of C# code as follows. This code summarizes a column of 'doubles' in a DataTable : This code takes 4 seconds to execute. I wanted to speed it up, so I ...
The Julia package ecosystem contains quite a few GPU-related packages and wrapper libraries, targeting different levels of abstraction. The packages below are precompiled in the container to provide users easy access to Nvidia highly parallel GPUs for accelerated computing. ...
The Julia package ecosystem contains quite a few GPU-related packages and wrapper libraries, targeting different levels of abstraction. The packages below are precompiled in the container to provide users easy access to Nvidia highly parallel GPUs for accelerated computing....
you will need to implement 3 different parallel loop solutions for the Floyd's algorithm using the MPI library on Julia. For each of the 3 implementations, you will need to use and explore different functions of the MPI wrapper on Julia, such as collectives and point-to-point communication....