This document discusses parallel computing concepts like MPI, data distributions, scatter, gather, allgather operations, and matrix-vector multiplication. It explains how to distribute data across processes, use MPI routines to share data, implement parallel matrix-vector multiplication, and measure performance of serial vs parallel versions. Derivative datatypes are also introduced to efficiently describe complex data structures for communication.
Multi Layered Perception is a very important chapter in applied Machine Learning using Python course of Handson School of Data Science Management and Technology.
The Raspberry Pi is a series of credit card–sized single-board computers developed in the UK by the Raspberry Pi Foundation with the intention of promoting the teaching of basic computer science in schools.
The original Raspberry Pi and Raspberry Pi 2 are manufactured in several board configurations through licensed manufacturing agreements with Newark element14 (Premier Farnell), RS Components and Egoman. These companies sell the Raspberry Pi online. Egoman produces a version for distribution solely in China and Taiwan, which can be distinguished from other Pis by their red colouring and lack of FCC/CE marks. The hardware is the same across all manufacturers.
The original Raspberry Pi is based on the Broadcom BCM2835 system on a chip (SoC), which includes an ARM1176JZF-S 700 MHz processor, VideoCore IV GPU, and was originally shipped with 256 megabytes of RAM, later upgraded (models B and B+) to 512 MB. The system has Secure Digital (SD) (models A and B) or MicroSD (models A+ and B+) sockets for boot media and persistent storage.
This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
Video available at : https://www.youtube.com/watch?v=EoysuTMmmMc
Multi Layered Perception is a very important chapter in applied Machine Learning using Python course of Handson School of Data Science Management and Technology.
The Raspberry Pi is a series of credit card–sized single-board computers developed in the UK by the Raspberry Pi Foundation with the intention of promoting the teaching of basic computer science in schools.
The original Raspberry Pi and Raspberry Pi 2 are manufactured in several board configurations through licensed manufacturing agreements with Newark element14 (Premier Farnell), RS Components and Egoman. These companies sell the Raspberry Pi online. Egoman produces a version for distribution solely in China and Taiwan, which can be distinguished from other Pis by their red colouring and lack of FCC/CE marks. The hardware is the same across all manufacturers.
The original Raspberry Pi is based on the Broadcom BCM2835 system on a chip (SoC), which includes an ARM1176JZF-S 700 MHz processor, VideoCore IV GPU, and was originally shipped with 256 megabytes of RAM, later upgraded (models B and B+) to 512 MB. The system has Secure Digital (SD) (models A and B) or MicroSD (models A+ and B+) sockets for boot media and persistent storage.
This slides explains how Convolution Neural Networks can be coded using Google TensorFlow.
Video available at : https://www.youtube.com/watch?v=EoysuTMmmMc
Avi Pfeffer, Principal Scientist, Charles River Analytics at MLconf SEA - 5/2...MLconf
Practical Probabilistic Programming with Figaro: Probabilistic reasoning enables you to predict the future, infer the past, and learn from experience. Probabilistic programming enables users to build and reason with a wide variety of probabilistic models without machine learning expertise. In this talk, I will present Figaro, a mature probabilistic programming system with many applications. I will describe the main design principles of the language and show example applications. I will also discuss our current efforts to fully automate and optimize the inference process.
INET is an internet framework for OMNeT++ simulator.
This is a guide to start INET.
INET for Dummies.
INET Tutorial
If this guide is hard for you to follow, please comment or give any suggestion.
Braxton McKee, CEO & Founder, Ufora at MLconf NYC - 4/15/16MLconf
Say What You Mean: Scaling Machine Learning Algorithms Directly from Source Code: Scaling machine learning applications is hard. Even with powerful systems like Spark, Tensor Flow, and Theano, the code you write has more to do with getting these systems to work at all than it does with your algorithm itself. But it doesn’t have to be this way!
In this talk, I’ll discuss an alternate approach we’ve taken with Pyfora, an open-source platform for scalable machine learning and data science in Python. I’ll show how it produces efficient, large scale machine learning implementations directly from the source code of single-threaded Python programs. Instead of programming to a complex API, you can simply say what you mean and move on. I’ll show some classes of problem where this approach truly shines, discuss some practical realities of developing the system, and I’ll talk about some future directions for the project.
TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Py...Edureka!
This Edureka TensorFlow Tutorial (Blog: https://goo.gl/HTE7uB) will help you in understanding various important basics of TensorFlow. It also includes a use-case in which we will create a model that will differentiate between a rock and a mine using TensorFlow. Below are the topics covered in this tutorial:
1. What are Tensors?
2. What is TensorFlow?
3. TensorFlow Code-basics
4. Graph Visualization
5. TensorFlow Data structures
6. Use-Case Naval Mine Identifier (NMI)
An Introduction to TensorFlow architectureMani Goswami
Introduces you to the internals of TensorFlow and deep dives into distributed version of TensorFlow. Refer to https://github.com/manigoswami/tensorflow-examples for examples.
Avi Pfeffer, Principal Scientist, Charles River Analytics at MLconf SEA - 5/2...MLconf
Practical Probabilistic Programming with Figaro: Probabilistic reasoning enables you to predict the future, infer the past, and learn from experience. Probabilistic programming enables users to build and reason with a wide variety of probabilistic models without machine learning expertise. In this talk, I will present Figaro, a mature probabilistic programming system with many applications. I will describe the main design principles of the language and show example applications. I will also discuss our current efforts to fully automate and optimize the inference process.
INET is an internet framework for OMNeT++ simulator.
This is a guide to start INET.
INET for Dummies.
INET Tutorial
If this guide is hard for you to follow, please comment or give any suggestion.
Braxton McKee, CEO & Founder, Ufora at MLconf NYC - 4/15/16MLconf
Say What You Mean: Scaling Machine Learning Algorithms Directly from Source Code: Scaling machine learning applications is hard. Even with powerful systems like Spark, Tensor Flow, and Theano, the code you write has more to do with getting these systems to work at all than it does with your algorithm itself. But it doesn’t have to be this way!
In this talk, I’ll discuss an alternate approach we’ve taken with Pyfora, an open-source platform for scalable machine learning and data science in Python. I’ll show how it produces efficient, large scale machine learning implementations directly from the source code of single-threaded Python programs. Instead of programming to a complex API, you can simply say what you mean and move on. I’ll show some classes of problem where this approach truly shines, discuss some practical realities of developing the system, and I’ll talk about some future directions for the project.
TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Py...Edureka!
This Edureka TensorFlow Tutorial (Blog: https://goo.gl/HTE7uB) will help you in understanding various important basics of TensorFlow. It also includes a use-case in which we will create a model that will differentiate between a rock and a mine using TensorFlow. Below are the topics covered in this tutorial:
1. What are Tensors?
2. What is TensorFlow?
3. TensorFlow Code-basics
4. Graph Visualization
5. TensorFlow Data structures
6. Use-Case Naval Mine Identifier (NMI)
An Introduction to TensorFlow architectureMani Goswami
Introduces you to the internals of TensorFlow and deep dives into distributed version of TensorFlow. Refer to https://github.com/manigoswami/tensorflow-examples for examples.
Inter-Process Communication in distributed systemsAya Mahmoud
Inter-Process Communication is at the heart of all distributed systems, so we need to know the ways that processes can exchange information.
Communication in distributed systems is based on Low-level message passing as offered by the underlying network.
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1. Parallel Computing
Mohamed Zahran (aka Z)
mzahran@cs.nyu.edu
http://www.mzahran.com
CSCI-UA.0480-003
MPI - III
Many slides of this
lecture are adopted
and slightly modified from:
• Gerassimos Barlas
• Peter S. Pacheco
4. Different partitions of a 12-
component vector among 3 processes
• Block: Assign blocks of consecutive components to each process.
• Cyclic: Assign components in a round robin fashion.
• Block-cyclic: Use a cyclic distribution of blocks of components.
6. Scatter
• Read an entire vector on process 0
• MPI_Scatter sends the needed
components to each of the other
processes.
# data items going
to each process
Important:
• All arguments are important for the source process (process 0 in our example)
• For all other processes, only recv_buf_p, recv_count, recv_type, src_proc,
and comm are important
8. • send_buf_p
– is not used except by the sender.
– However, it must be defined or NULL on others to make the
code correct.
– Must have at least communicator size * send_count elements
• All processes must call MPI_Scatter, not only the sender.
• send_count the number of data items sent to each process.
• recv_buf_p must have at least send_count elements
• MPI_Scatter uses block distribution
9. 0 21 3 4 5 6 7 0
0 1 2
3 4 5
6 7 8
Process 0
Process 0
Process 1
Process 2
10. Gather
• MPI_Gather collects all of the
components of the vector onto process
dest process, ordered in rank order.
Important:
• All arguments are important for the destination process.
• For all other processes, only send_buf_p, send_count, send_type, dest_proc,
and comm are important
number of elements
for any single receive
number of elements
in send_buf_p
20. Keep in mind …
• In distributed memory systems,
communication is more expensive than
computation.
• Distributing a fixed amount of data
among several messages is more
expensive than sending a single big
message.
23. MPI_Type create_struct
• Builds a derived datatype that consists
of individual elements that have
different basic types.
From the address of item 0an integer type that is big enough
to store an address on the system.
Number of elements in the type
24. Before you start using your new data type
Allows the MPI implementation to
optimize its internal representation of
the datatype for use in communication
functions.
38. Conclusions
• Reducing messages is a good
performance strategy!
– Collective vs point-to-point
• Distributing a fixed amount of data
among several messages is more
expensive than sending a single big
message.
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