Techila End-User Training

Transcription

Techila End-User Training
What Is Techila
Techila is a HPC middleware/ resource
manager, which enables applications to
harness computing capacity securely and
without complexity.
The content of this presentation is confidential. Using, copying, or giving any part of this presentation or its description to a third party is strictly forbidden without the prior written consent of Techila Technologies Ltd.
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Techila Architecture
 End-User
 Researcher
 Using
MATLAB/Python/etc. on
their computer
 Techila Server
 Manages computational
resources
 Distributes
computational Jobs
 Techila Workers
 Provides computational
power
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IT infrastructure
The End-User working on
projects, which could benefit
of scalable distributed
computing capacity.
End-User with standard
computational tools (MATLAB,
R,…)
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IT infrastructure
The End-User downloads Techila SDK
from the Techila Extranet
www.techilatechnologies.com >
Extranet
R,…)
and installs the SDK on their
computer. The Techila SDK offers
integration to standard
computational tools and languages.
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Techila Worker
Techila Worker
Techila Worker
Techila environment
Techila Worker
Techila Server
R,…)
Techila Worker
The Techila environment consists
of a Techila Server and several
computers providing the
computing power (Workers).
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Techila environment
Techila Server
SSL
Techila SDK connects the local tools to
the Techila Server. Techila does
automatic distribution of the Project.
R,…)
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Techila
environment
SSL
SSL
SSL
Techila Server
SSL
R,…)
SSL
SSL
The Techila Server configures
the Workers to execute the
computational Project.
All connections are secured
and all data packages are
signed.
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Techila
environment
SSL
SSL
SSL
Techila Server
SSL
R,…)
SSL
SSL
Workers return their
results to the Techila
Server, which returns the
results to the End-User’s
computer.
The results can be
streamed as they become
available or returned all
at the same time or
fetched from the Techila
Server at a later point of
time.
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Techila environment
Techila Server
The End-User can start
analyzing the results without
having to wait.
R,…)
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Automatic Worker Configuration
W
W
W
Techila
Server
W
W
W
W
W

Techila Scheduler Overview:
http://tinyurl.com/techilascheduler
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DLL
Peach wrapper
Java peach
Java API
Shell / Command Prompt
C#
Techila layer
Java
Shell / Command Prompt
FORTRAN
C/C++/Other (supporting DLLs)
Python
Python peach
R peach
rJava
R
Perl peach
Perl
End-User layer
Inline Java
MATLAB peach
MATLAB
Techila API Support
CLI
Techila native layer
Third party component
Recommended API
.NET
peach
CLI
.NET API
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Distributed Computing Problems
 Parallel Problems
 Jobs depend on each other’s states
 Communication between jobs
 For example fluid dynamics
 Embarrassingly Parallel Problems
 Jobs are totally independent
 No communication between jobs
 For example Monte Carlo
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Techila Terminology
 Project
 Computational problem that consists
of several Jobs
 Project parameters can be used to
enable Techila features
 Jobs
 Independent computational tasks
 Deployed and solved on the Workers
 One Job is deployed on one CPU core
Project
Jobs
Bundles
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Techila Terminology
 Bundles
 Created automatically when creating a
Project
 Containers for data, binaries, libraries
 Used to transfer necessary
components to Workers
 All required Bundles are transferred
automatically to Workers
 Deployed from Techila Server to Workers
on-demand
Project
Jobs
Bundles
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Techila SDK
 Enables you to access the Techila
environment
 Can be downloaded from the Techila Extranet:
http://www.techilatechnologies.com/extranet/
 Contains:
 Documents for getting started with Techila
 Examples for several programming languages,
including MATLAB and Python
 End-User Guides for MATLAB, Python and the
CLI with walkthroughs of the examples
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Techila Helper Functions
 Peach
 Name from "Parallel EACH"
 Can be used to execute computationally intensive
functions/programs in the Techila environment
 Typically used for parameter sweep type
computations
 Cloudfor
 Can be used to distribute computationally intensive
for-loop structures
 Currently available for MATLAB and R
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Techila-Enabling Your Application
1. Locate the computationally intensive part of your application
 When available, use profiling tools included in the IDE such as the MATLAB
profiler
 When profiling tools not available, use debug printouts with timestamps
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2. Choose the Techila helper function and modify the code
 Cloudfor for distributing computationally intensive for-loops
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2. Choose the Techila helper function and modifying the code
 Peach for executing functions used e.g. for parameter sweeping
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3. Create the computational Project.
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Features:
 Can be used to improve efficiency and implement
more complex distributions
 Features include:
 Streaming
 Snapshots
 Callback functions
 Job-specific input files
 Distributing precompiled binaries
 Detached Projects
 Iterative Projects
 ...
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Features: Streaming
 Transfer results from the Techila environment as soon as they are
available
 Enables post-processing job results before the project is completed
 Saves result transfer time when the results are large
With streaming
Without streaming
End-user
Server
Workers
End-user
Server
Create project
Create project
Transfer jobs to workers
Transfer result
Stream result
Transfer result
Download results
Workers
Transfer result
Transfer result
Stream result
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Features: Callback
 Easy way to post-process results
 Each job result is delivered to the callback function
 Results from the callback function are optionally returned from
peach()
End-user
Server
Workers
 For example:
Create project
 To plot the results in a
continuous manner
 To remove parts of the result
Transfer result
to files and
Stream result
Callback
return other parts from
Post-processed result
Transfer result
Stream result
peach()
Callback
Post-processed result
 To save memory
 To manage large result files
With streaming and callback
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Features: Snapshots
 Saving the state of the job
End-user
 Requires support in
computation code
 To save the state
 To resume from the
saved state
 Saves time in long runs
 Resuming after reboot
 Resuming on another
Worker
 Optimizing  transfer to a
faster Worker
Server
Workers
Create project
Download results
Transfer job to a worker
Upload snapshot
Upload snapshot
Computation interrupted
Transfer job to a worker
Download latest snapshot
Computation resumed
using the latest snapshot
Transfer result
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Features: Job Input Files



Enables job-specific data files
 Other bundles are deployed
to each participating Worker
 Job Input Bundle is
transferred only to the server
 Individual files from the
Bundle are deployed to the
Workers
Saves memory
 Can be used to load only the
data required by the job
Saves time and network
bandwidth
 Only the file needed by the
job is transferred from the
server
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Features: Precompiled Binaries
 Using MATLAB/Perl/Command Line Interface as
the front-end
 Compute with precompiled binary (Fortran, C/C++,
etc.)
 Optionally post-process with MATLAB/Perl
 Easy way to execute code written in languages not
having peach() (yet)
 Possible to execute the computation on multiple
platforms
 Requires a precompiled binary for each of the
platforms
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Features: Project Detaching
 No need to wait for the projects online
 Start the long project from laptop, turn off the laptop and
have a nice weekend
 Fetch the results on Monday
End-user
Server
Workers
End-user
Server
Workers
Create project
Create project
Transfer result
Transfer result
Download results
Transfer result
Transfer result
Download request
Download results
Normal Convention
Detached Project
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Features: Iterative Projects
 Technique for creating
several, consecutive
Projects
 Achieved by placing
the cloudfor-loop or
peach-function inside a
loop structure
 Results of previous
Projects used as input
data in the next Project
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CONFIDENTIAL
Feature: Interconnect
 Allows Jobs within the
same Project to
communicate with each
other
 Interconnect
communication always
adds some network
communication
overhead
 Performing too frequent
interconnect data
transfers can impact
performance
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Techila API - MATLAB Cloudfor
 Provides a simple way to distribute for-loop structures
 Should only be used on computationally intensive loops
 Iterations must be independent
 Cloudfor loops marked with 'cloudfor' and 'cloudend' keywords
 Nested cloudfor loops possible
result=zeros(1,10);
for k=1:10
result(k)=k*k;
end
result=zeros(10,10);
for k=1:10
for j=1:10
result(k,j)=k*j;
end
end
Cloudfor
result=zeros(1,10);
cloudfor k=1:10
result(k)=k*k;
cloudend
Cloudfor
result=zeros(10,10);
cloudfor k=1:10
cloudfor j=1:10
result(k,j)=k*j;
cloudend
cloudend
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Techila API - MATLAB Peach
 Simple interface for executing a computationally intensive function
on Workers
 Peach function syntax typically defines the following parameters:
 The name of the function that will be deployed and executed on
Workers
 Input arguments for the executable function
 List of data files that should be transferred to the Workers (optional)
 Number of Jobs in the Project
function result = run_test()
var = 5;
result = peach('workerFunction',...
{var,'<param>'},...
{'datafile.txt'},...
1:10)
end
%
%
%
%
Executable function
Input arguments
Additional data files
Number of Jobs
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Techila API - R Cloudfor
 Simple interface for distributing computationally intensive forloop structures
 Automatically detects which variables need to be transferred to
Workers
 Computationally light iterations can be grouped to increase
performance
 Possible to use nested cloudfor loops
 Iterations must be independent
for (i in 1:10) {
result[i] <- i*i
}
result <- matrix(NA,5,10)
for (i in 1:10) {
for (j in 1:5) {
result[j,i] <- i*j
}
}
Cloudfor
library(techila)
result <- cloudfor (i=1:10) %t% {
i*i
}
Cloudfor
library(techila)
result <- cloudfor (i = 1:10) %to%
cloudfor (j = 1:5) %t% {
i*j
}
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Techila API - R Peach
 Simple interface for executing computationally intensive functions
on Workers
 At minimum, the peach-function syntax typically defines the
following parameters:
 The name of the function that will be executed on Workers
 List of data files that should be transferred to the Workers
library(techila)
var1 <- 5
jobs <- 10
# Load the Techila library
result <- peach(funcname = "workerFunction",
params = list(var1, "<param>"),
files = list("file_dist.r"),
peachvector = 1:jobs,
sdkroot="C:/techila") # Path to
# Executable function
# Input arguments
# File to be sourced
# Number of Jobs
the 'techila' directory
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Techila API - Python Peach
 Simple interface for executing a computationally intensive function on
Workers
 Peach function syntax typically defines following parameters:
 Name of the executable function that will be executed on Workers
 Python-script containing executable function definitions
 List of data files that should be transferred to Workers
import techila
var = 5
result = techila.peach(funcname = 'exec_func',
#
params = [var,'<vecidx>'], #
files = ['ex_file.py'],
#
datafiles = ['datafile.txt'],
peachvector = range(0, 10))
Executable function
Function input arguments
Function definitions
# Additional data files
# Job count & arguments
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Techila API - CLI Peach
 Simple interface for distributing computations from the
command line (eg. Windows command prompt / Linux shell)
 At its simplest form, the peach command typically defines:
 The name of the executable program
 Input arguments for the executable program
 Files that will be transferred to the participating Workers
 Name of the output file
java -jar C:\techila\lib\techila.jar peach \
command="binary;osname=Linux,binary.exe;osname=Windows" \
parameters="5 %P(peachparam)" \
platform="Windows;Linux" \
outputfiles="output;file=output_1" \
peachvector="1 2 3 4 5" \
datafile1.txt datafile2.txt
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Techila End-User Training

Transcription

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