I'm one of the maintainers at Google of the LLVM NVPTX backend. Happy to answer questions about it.
As background, Nvidia's CUDA ("CUDA C++?") compiler, nvcc, uses a fork of LLVM as its backend. Clang can also compile CUDA code, using regular upstream LLVM as its backend. The relevant backend in LLVM was originally contributed by nvidia, but these days the team I'm on at Google is the main contributor.
I don't know much (okay, anything) about Julia except what I read in this blog post, but the dynamic specialization looks a lot like XLA, a JIT backend for TensorFlow that I work on. So that's cool; I'm happy to see this work.
Full debug information is not supported by the LLVM NVPTX back-end yet, so cuda-gdb will not work yet.
We'd love help with this. :)
Bounds-checked arrays are not supported yet, due to a bug [1] in the NVIDIA PTX compiler. [0]
We ran into what appears to be the same issue [2] about a year and a half ago. nvidia is well aware of the issue, but I don't expect a fix except by upgrading to Volta hardware.
[0] https://julialang.org/blog/2017/03/cudanative [1] https://github.com/JuliaGPU/CUDAnative.jl/issues/4 [2] https://bugs.llvm.org/show_bug.cgi?id=27738
I've always thought it weird that I'm writing all my code in this language that compiles to C++, with semantics for any type declaration etc...And then I write chunks of code in strings, like an animal.
[1] https://github.com/ldc-developers/ldc [2] dlang.org [3] http://github.com/libmir/dcompute
The NVPTX backend would benefit imo to move towards the more general LLVM infrastructure so that emitting the dwarf info is not another special case.
To be clear, there are two ways to compile CUDA (C++) code. You can either use nvcc (which itself may use clang), or you can use regular, vanilla clang, without ever involving nvcc.
Nvidia's closed-source compiler, nvcc, uses your host (i.e. CPU) compiler (gcc or clang) because it transforms your input .cu file into two files, one of which it compiles for the GPU (using a program called cicc), and the other of which it compiles for the CPU using the host compiler.
The other way to do it is to use regular open-source clang without ever involving nvcc. The version of clang that comes with your xcode may not be new enough (I dunno), but the LLVM 5.0 release should be plenty new, unless you want to target CUDA 9, in which case you'll need to build from head.
I don't know the technical reasons why nvcc is so closely tied to the host compiler version -- it annoys me sometimes, too.
The hard part is optimization, because the GPU architecture (SIMD / SIMT) is so alien compared to normal CPUs.
Here's a step-by-step example of one guy optimizing a Matrix Multiplication scheme in OpenCL (specifically for NVidia GPUs): https://cnugteren.github.io/tutorial/pages/page1.html
Just like how high-performance CPU computing requires a deep understanding of cache and stuff... high-performance GPU computing requires a deep understanding of the various memory-spaces on the GPU.
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Now granted: deep optimization of routines on CPUs is similarly challenging, and actually undergoes a very similar process in how to partition your work problem into L1-sized blocks. But high-performance GPUs not only have to consider their L1 Cache... but also "Shared" (or OpenCL __local) memory and "Register" (or OpenCL __private) memory as well. Furthermore, GPUs in my experience have way less memory than CPUs per thread/shader. IE: Intel "Sandy Bridge" CPU has 64kb L1 cache per core, which can be used as 2-threads if hyperthreading is enabled. A "Pascal" GPU has 64kb of "Shared" memory, which is extremely fast like L1 cache. But this 64kb is shared between 64 FP32 cores!!!.
Furthermore, not all algorithms run faster on GPGPUs either. For example:
https://askeplaat.files.wordpress.com/2013/01/ispa2015.pdf
This paper claims that their GPGPU implementation (Xeon Phi) was slower than the CPU implementation! Apparently, the game of "Hex" is hard to parallelize / vectorize.
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Now don't get me wrong, this is all very cool and stuff. Making various programming tasks easier is always welcome. Just be aware that GPUs are no silver bullet for performance. It takes a lot of work to get "high-performance code", regardless of your platform.
And sometimes, CPUs are faster.
Wow. That's very impressive.
I hope one day we get this sort of tooling with AMD GPUs.
