Skip to content
Sample python code - implementation differences between CPU, GPU and DASK
Python
Branch: master
Clone or download

Latest commit

Fetching latest commit…
Cannot retrieve the latest commit at this time.

Files

Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
images_folder
.gitignore
CalcCore.py
README.md
cuda-demo.py
dask-demo.py

README.md

Execution Environment

Docker

You can run these from the stock Anaconda 3 docker container. Numba is already installed.
Use their instructions or docker-compose up using this docker-scripts repository. A guide can also be found on my blog.

Note GPU drivers are not available as pass through when running containers on Windows 10 systems (as of 3/2020)

Linux install.

Install linux using the anaconda 3 instructions on the anaconda web site. You can also look on by blog for links and linux instructions.

cuda-demo and dask-demo numbers

These numbers represent the amount of time it takes to iterate 10,000,000 times using various approaches

Program Structure

The various approaches are structured as similar as possible without walking away from implementation specific optimizations. Program Flow

Algorithm

  • 10,000,000 long vector [[a,b,c],[a,b,c],...]
  • Calculate math.log(param_triple[0])*math.log(param_triple[1])*math.log(param_triple[2])
  • Sum the results of all operations

Test Machine

  • Z820 2X E5-2640 v2 @ 2.00GHz 128GB memory
  • 2 - 8 Core Hyper threaded
  • 32 Virtual Cores
  • Nvidia RTX 2060 Super

Sample times

cuda-demo.py

GPU grid:            0.1630   Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
CPU jit parallel:    0.0922   Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
CPU jit serial:      1.1733   Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
CPU serial nojit:    12.4751  Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297

dask-demo.py

DASK jit thrd multi   2.5249   Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
DASK jit thrd sing:   1.7951   Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
DASK jit proc mult:   1.9432   Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
DASK jit proc sing:   3.8063   Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297

CPU jit parallel:     0.0922   Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
CPU jit serial:       1.1381   Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
CPU nojit serial:     10.7825  Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297

DASK threading

  • Multi-threaded: 8 processors 32 threads
  • Single-threaded: 1 processor 1 thread

Test Machine

Dell Latitude I5-4310U @ 2.00Ghz / 2.60Ghz Docker 2 cores 4GB

Sample times

dask-demo.py

DASK thrd multi: 1.6286  Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
DASK thrd sing:  1.2028  Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
DASK proc mult:  3.0543  Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
DASK proc sing:  2.6753  Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297

CPU jit parallel:  0.4176  Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
CPU jit serial:    0.6670  Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297
CPU serial nojit:  13.8456  Iterations: 10,000,000  dtype= float64 numcalc: sum( 10,000,000 ): 34986983045.74297

DASK threading

  • Multi-threaded: 2 processors 2 threads total
  • Single-threaded: 1 processor 1 thread total
You can’t perform that action at this time.