#ARRAYSYNC ALTERNATIVE CODE#

It offers an abstract array implementation tailored towards using the raw power of highly parallel hardware. GPUArrays.jl is that foundation in Julia. So most algorithms will need arrays to manage all that data, which calls for a good GPU array library as a crucial foundation. Most highly parallel algorithms need to churn through quite a bit of data to overcome all the threading and latency overheads.

#ARRAYSYNC ALTERNATIVE CODE#

The good news is Julia, a high level scripting language, allows you to write both kernel and surrounding code in Julia itself, while running on most GPU hardware! GPUArrays One needs to decide what to use, and will get pretty much stuck with that decision While CUDA only supports Nvidia hardware, OpenCL supports all hardware but is a bit rough around the edges. There is a divide between CUDA and OpenCL, which are the dominant frameworks used to write low-level GPU code. Kernels are usually written in a C/C++ dialect, which is not the nicest language to write down your algorithms For more details on what to expect, have a look at this blog post Related to the above, a lot of algorithms won't port nicely to the GPU. GPU functions (kernels) are inherently parallel, so writing GPU kernels is at least as difficult as writing parallel CPU code, but the difference in hardware adds quite a bit of complexity Lower precision is the default and higher precision computations can easily void all performance gains Setting up a kernel can quickly become complicated without a high level wrapper Times are around ~10us for GPUs, compared to a few nano seconds on a CPU Even launching a kernel on the GPU (in other words scheduling a function call) introduces large latencies. As a result, there are long transfer times from RAM to the GPUs memory (VRAM). Let's have a detailed look at the disadvantages one buys into when utilizing this raw power:Ī GPU is a separate piece of hardware with its own memory space and different architecture. The sheer number of parallel threads one can launch can yield massive speed-ups, but also makes it harder to utilize the GPU.

Arraysync alternative