Python at Warp Speed

Python at Warp Speed
Andreas Schreiber
Department for Intelligent and Distributed Systems
German Aerospace Center (DLR), Cologne/Berlin
> PyCon DE 2016 > Andreas Schreiber • Python at Warp Speed > 30.10.2016DLR.de • Chart 1

Introduction
Scientist,
Head of department
Co-Founder, Data
Scientist, and Patient
Communities

Python at Warp Speed
High-
Performance
Computing
Distributed
Computing
Quantum
Computing

Algorithmic View
Input x – Algorithm A – Output y
High-Perf.
Computing
•
Compute
Distributed
Computing
•
Learn
Quantum
Computing
•
Optimize

High-Performance
Computing

High raw computing power for large science applications
• Huge performance on a single / multi-core processors
• Huge machines with up to millions of cores
High-Performance Computing
Images: DLR & https://sciencenode.org

Sunway TaihuLight
10.649.600 Cores, 93 PetaFLOPS

Tianhe-2 (天河二号)
3.120.000 Cores, 33,8 PetaFLOPS

Titan
560.640 Cores, 17,5 PetaFLOPS

Programming HPC
Technologies
• MPI (Message Passing Interface)
• OpenMP (Open Multi-Processing)
• OpenACC (Open Accelerators)
• Global Arrays Toolkit
• CUDA (GPGPUs)
• OpenCL (GPGPUs)
Languages
• Fortran
• Fortran
• C/C++
• Python

Performance Fortran vs. Python
Helicopter Simulation
Fortran Code
• Developed 1994-1996
• parallelized with MPI
• Performance optimization
2013/14 with MPI und
OpenACC
Performance Comparison
• Multi-core CPUs
• Cython mit OpenMP
• Python bindings for Global
Array Toolkit
• GPGPUs
• NumbaPro

Core Computation Loops in Pure Python
for iblades in range(numberOfBlades):
for iradial in range(1, dimensionInRadialDirection):
for iazimutal in range(dimensionInAzimualDirectionTotal):
for i1 in range(len(vx[0])):
for i2 in range(len(vx[0][0])):
for i3 in range(len(vx[0][0][0])):
# wilin-Aufruf 1
for iblades in range(numberOfBlades):
for iradial in range(dimensionInRadialDirection):
for iazimutal in range(1, dimensionInAzimualDirectionTotal):
for i1 in range(len(vx[0])):
for i2 in range(len(vx[0][0])):
for i3 in range(len(vx[0][0][0])):
# wilin-Aufruf 2
for iDir in range(3):
for i in range(numberOfBlades):
for j in range(dimensionInRadialDirection):
for k in range(dimensionInAzimualDirectionTotal):
x[iDir][i][j][k] = x[iDir][i][j][k] +
dt * vx[iDir][i][j][k]

Single Core (Xeon E5645, 6 Cores)
2.51
1.09
0.27
0.46
0.01
0
0.5
1
1.5
2
2.5
3
Fortran Cython Numba Numpy Python
GFlops

Multi-Core (Xeon E5645, 6 Cores)
13.64
5.78
1.38
0
2
4
6
8
10
12
14
16
Fortran Cython Global Arrays
GFlops

GPGPU (NVIDIA Tesla C2075, 448 CUDA-Cores)
69.77
7.79
0
10
20
30
40
50
60
70
80
Fortran Numba
GFlops

Performance-Productivity-Space
C++ /
FORTRAN
Cython
NumPy /
Numba
Pure
Python
Performance
Productivity/Simplicity

Python’s productivity is great
• Allows to write code quickly
• Wide range of applications
Python’s performance still needs improvements
• Code optimization with tools for profiling, code examination, …
• Optimized libraries for parallel processing with MPI etc.
Excited to see advancements by community and companies
Productivity vs. Performance of Python

Annual scientific workshop, in conjunction
with Supercomputing conference
State-of-the-art in
• Hybrid programming
• Comparison with other languages for HPC
• Interactive parallel computing
• High-performance computing applications
• Performance analysis, profiling, and debugging
PyHPC 2016
• 6th edition, Nov 14, 2016, Salt Lake City
• http://www.dlr.de/sc/pyhpc2016
Workshop „Python for High-Performance and
Scientific Computing“ (PyHPC)

Tools Example
Intel® VTune™ Amplifier for Profiling
https://software.intel.com/en-us/intel-vtune-amplifier-xe/

Tools Example
mpi4py with Intel MPI Library and Cython
from mpi4py import MPI
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
name = MPI.Get_processor_name()
if rank == 0:
print "Rank %d of %d running on %s"
% (rank, size, name)
for i in xrange(1, size):
rank, size, name = comm.recv(source=i, tag=1)
print “Rank %d of %d running on %s"
% (rank, size, name)
else:
comm.send((rank, size, name), dest=0, tag=1)
http://pythonhosted.org/mpi4py/
https://software.intel.com/en-us/intel-mpi-library
mpi4py
Cython
Intel MPI
Library

Distributed Computing

Paul Baran. On Distributed Communication Networks. IEEE Transactions on Communications, 12(1):1–9, March 1964

Driven by data science, machine learning, predictive analytics, …
• Tabular data
• Time Series
• Stream data
• Connected data
Scaling up with increased data size
from from laptops to clusters
• Many-Task-Computing
• Distributed scheduling
• Peer-to-peer data sharing

Space Debris: Object Correlation from Sensor Data
and Real-Time Collision Detection

29,000 Objects Larger than 10 cm

750,000 Objects Larger than 1 cm

150M Objects Larger than 1 mm

Space Debris: Graph of Computations

Directed Acyclic Graph (DAG)
Python has great tools to execute graphs on distributed resources
Graphs
A
B
E F
G
D
C

PySpark
https://spark.apache.org/
https://spark.apache.org/docs/0.9.0/python-programming-guide.html
from pyspark import SparkContext
logFile = “myfile.txt“
sc = SparkContext("local", "Simple App")
logData = sc.textFile(logFile).cache()
numAs = logData.filter(lambda s: 'a' in s).count()
numBs = logData.filter(lambda s: 'b' in s).count()
print("Lines with a: %i, lines with b: %i" % (numAs, numBs))

Dask
http://dask.pydata.org/
import dask.dataframe as dd
df = dd.read_csv('2015-*-*.csv')
df.groupby(df.user_id).value.mean().compute()
d = {'x': 1,
'y': (inc, 'x'),
'z': (add, 'y', 10)}

TensorFlow
import tensorflow as tf
with tf.Session() as sess:
with tf.device("/gpu:1"):
matrix1 = tf.constant([[3., 3.]])
matrix2 = tf.constant([[2.],[2.]])
product = tf.matmul(matrix1, matrix2)
https://www.tensorflow.org/

Quantum Computing

Design optimization and robust design
• Space systems and air transportation systems
• Design and evaluation of systems with consideration of
uncertainties
Optimization Problems in Aeronautics & Space

Machine Learning
• Deep learning, pattern recognition, clustering, images recognition,
stream reasoning
Anomaly detection
• Monitoring of space systems
Mission planning
• Optimization in relation to time, resource allocation, energy
consumption, costs etc.
Verification and validation
• Software, embedded systems
Optimization Problems in Aeronautics & Space

Discrete optimization is basis for many kinds of problems
 Packaging
 Partitioning
 Mapping
 Scheduling
Hope: Quantum Computers solve those problems faster than classical
computers
New Research Field: Quantum Computing
NP-hard problems!

Bits and Qubits
Classical Bits Quantum bits (Qubits)
• “0” or “1”
• Electric voltage
• Superposition of complex base
states
1
0

Quantum Computer
To date, one Quantum Computer
is available commercially
• D-Wave Systems, Inc.,
Canada
• System with ~2000 Qubits
(„D-Wave 2X“)
• Adiabatic QC
Images: © D-Wave Systems, Inc.

D-Wave Qubit Topology – Chimera Graph

Rewrite the problem as discrete optimization problem
• QUBO: Quadratic unconstrained binary optimization
“Programming” a Quantum Computer – Step 1
𝐸 𝑞1, … , 𝑞 𝑛 = 𝑔𝑖
𝑛
𝑖=1
𝑞𝑖 + 𝑠𝑖𝑗 𝑞𝑖 𝑞𝑗
𝑛
𝑖,𝑗=1
𝑖>𝑗

Mapping to hardware
topology
 Chimera-QUBO

Bringing the problem to the QC
• Copy weights and coupling
strengths to physical Qubits

Starting the actual “computation”:
• Adiabatic process from start energy level to target energy level,
which represents solution of the optimization problem
• Result are the voltages after this process
Energy
Start System End System
Time
Energy Level
Adiabatic Change
Defines
runtime

D-Wave Software Environment

Programming with Python
Import API and Connect to Machine
import dwave_sapi2.remote as remote
import dwave_sapi2.embedding as embedding
import dwave_sapi2.util as util
import dwave_sapi2.core as core
# print "Connect to DWave machine ...”
# create a remote connection
try:
conn = remote.RemoteConnection(myToken.myUrl,
myToken.myToken)
# get the solver
solver = conn.get_solver('C12')
except:
print 'Unable to establish connection'
exit(1)

Prepare the Problem (“Embedding”)
hwa = get_hardware_adjacency(solver)
embeddings[eIndex] = embedding.find_embedding(J, hwa)
h_embedded[eIndex], j0, jc, new_embed =
embedding.embed_problem(h, J, embeddings[eIndex], hwa)
J_embedded[eIndex] = jc
J_embedded[eIndex].update(j0)
embeddings[eIndex] = new_embed

Solve the Problem (“Ising”)
getEmbeddedIsing(eIndex)
# print "Annealing ...”
result = core.solve_ising(solver,
h_embedded[eIndex],
J_embedded[eIndex],
annealing_time=20,
num_reads=1000)
unembedded_result = embedding.unembed_answer(
result['solutions'],
embeddings[eIndex],
'minimize_energy', h, J)
# take the lowest energy solution
rawsolution_phys = (np.array(result['solutions']) + 1)/2
rawsolution_log = (np.array(unembedded_result) + 1)/2

Result Distribution

Summary
High
Performance
Data Science
Future
Architectures
Python is or will become standard in programming for...

Thank You!
Questions?
Andreas.Schreiber@dlr.de
www.DLR.de/sc | @onyame

Python at Warp Speed

Recommended

More Related Content

What's hot (20)

Similar to Python at Warp Speed (20)

More from Andreas Schreiber (20)

Recently uploaded (20)

Python at Warp Speed