HPC 5

My Personal Notes from the Leeds University HPC5 training course

Course Link: goo.gl/NMGpWk

The code follows some of the tasks in the course work sheet. Commits every question and bugfix.

🚧 These are just notes from a course, this is not developed work 🚧

General GPU

• As clockspeeds stall best to move to accelerators
• ARC3 has the discontinued Xeon Phi nodes that go unused
• nVida produce Tesla GPUS like GeForce but for HPCS
• ARC3 has 6 GPU Nodes (4 P100s) and 2 ( 2 k80 ) nodes. ARC4 will have less.
  • #$ -l coproc_p100=1 Requests 1/4 = =4 gives whole
  • #$ -l coproc_k80=1 Requests 1/2 =2 gives whole
• ARC3 GPUs are under utilised
• Always use CPU and GPUs together the GPUs do the heavy lifting, CPU requirement depends on code

Coding

• Use Libraries e.g. MATLAB, Ansys
• Directives like OpenACC compile and auto gen
• CUDA extension to C for nVida whereas OpenCL works cross platform

CUDA

• Created by nVida
• Not super user friendly even PyCUDA
• Works by sending code to CPU and separate code to GPU Kernel
• Each Streaming Multiproc (SM) has multiple cores
• ARC3 p100s has 56 SMs with 64 single precision cores or 32 Double Precision cores
• CUDA vectors can help control location in GPU architecture

CUDA extra

• NB nvcc doesn't work with every version of every compiler and it's hard to find out which
• PyCUDA (includes GPU interactive sessions)
  • import pycuda.autoinit important!!
  • a_gpu = cuda.mem_alloc(a.nbytes) it's important to cast arrays to allocate memory
  • Still requires C for kernel but pro is that skip the compiling code and allows for using numpy functions
• CUDApython (numba)
• Alexnet example on github
  • tutorial

Useful links

• NVIDA ecosystem has a whole suite of accelerated code and libraries etc.

Trouble shooting

• Compiler requires .cu extension
• tabs are not accepted

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Acknowledgements

The course was from the ARC Training Course: HPC5, delivered by Martin Callaghan.