1. This document introduces parallel computing, which involves dividing large problems into smaller concurrent tasks that can be solved simultaneously using multiple processors to reduce computation time.
2. Parallel computing systems include single machines with multi-core CPUs and computer clusters consisting of multiple interconnected machines. Common parallel programming models involve message passing between distributed memory processors.
3. Performance of parallel programs is measured by metrics like speedup and efficiency. Factors like load balancing, serial fractions of problems, and parallel overhead affect how well a problem can scale with additional processors.
Please contact me to download this pres.A comprehensive presentation on the field of Parallel Computing.It's applications are only growing exponentially day by days.A useful seminar covering basics,its classification and implementation thoroughly.
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Research Scope in Parallel Computing And Parallel ProgrammingShitalkumar Sukhdeve
Research Scope in Parallel Programming and Parallel computing,Different forms of parallel computing,bit-level,
instruction level, data, and task parallelism,multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, MPPs, and grids ,Concurrent programming languages, libraries, APIs, and parallel programming models (such as Algorithmic Skeletons) ,shared memory, distributed memory, or shared distributed memory.POSIX Threads and OpenMP are two of most widely used shared memory APIs, whereas Message Passing Interface (MPI) is the most widely used message-passing system API.The ”future concept” is also useful while implementing parallel programming.Automatic parallelization,parallel programming languages exist—
SISAL,
Parallel Haskell,
System C (for FPGAs),
Mitrion-C,
VHDL, and
Verilog.
Application checkpointing
The primary reasons for using parallel computing:
Save time - wall clock time
Solve larger problems
Provide concurrency (do multiple things at the same time)
Parallel computing and its applicationsBurhan Ahmed
Parallel computing is a type of computing architecture in which several processors execute or process an application or computation simultaneously. Parallel computing helps in performing large computations by dividing the workload between more than one processor, all of which work through the computation at the same time. Most supercomputers employ parallel computing principles to operate. Parallel computing is also known as parallel processing.
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Please contact me to download this pres.A comprehensive presentation on the field of Parallel Computing.It's applications are only growing exponentially day by days.A useful seminar covering basics,its classification and implementation thoroughly.
Visit www.ameyawaghmare.wordpress.com for more info
Research Scope in Parallel Computing And Parallel ProgrammingShitalkumar Sukhdeve
Research Scope in Parallel Programming and Parallel computing,Different forms of parallel computing,bit-level,
instruction level, data, and task parallelism,multi-core and multi-processor computers having multiple processing elements within a single machine, while clusters, MPPs, and grids ,Concurrent programming languages, libraries, APIs, and parallel programming models (such as Algorithmic Skeletons) ,shared memory, distributed memory, or shared distributed memory.POSIX Threads and OpenMP are two of most widely used shared memory APIs, whereas Message Passing Interface (MPI) is the most widely used message-passing system API.The ”future concept” is also useful while implementing parallel programming.Automatic parallelization,parallel programming languages exist—
SISAL,
Parallel Haskell,
System C (for FPGAs),
Mitrion-C,
VHDL, and
Verilog.
Application checkpointing
The primary reasons for using parallel computing:
Save time - wall clock time
Solve larger problems
Provide concurrency (do multiple things at the same time)
Parallel computing and its applicationsBurhan Ahmed
Parallel computing is a type of computing architecture in which several processors execute or process an application or computation simultaneously. Parallel computing helps in performing large computations by dividing the workload between more than one processor, all of which work through the computation at the same time. Most supercomputers employ parallel computing principles to operate. Parallel computing is also known as parallel processing.
↓↓↓↓ Read More:
Watch my videos on snack here: --> --> http://sck.io/x-B1f0Iy
@ Kindly Follow my Instagram Page to discuss about your mental health problems-
-----> https://instagram.com/mentality_streak?utm_medium=copy_link
@ Appreciate my work:
-----> behance.net/burhanahmed1
Thank-you !
Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
- In last few years, rapidly increasing businesses
and their capabilities & capacities in terms of computing has
grown in very large scale. To manage business requirements
High performance computing with very large scale resources is
required. Businesses do not want to invest & concentrate on
managing these computing issues rather than their core business.
Thus, they move to service providers. Service providers such as
data centers serve their clients by sharing resources for
computing, storage etc. and maintaining all those.
Parallel computing is a type of computation in which many calculations or the execution of processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has been employed for many years, mainly in high-performance computing, but interest in it has grown lately due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.
- In last few years, rapidly increasing businesses
and their capabilities & capacities in terms of computing has
grown in very large scale. To manage business requirements
High performance computing with very large scale resources is
required. Businesses do not want to invest & concentrate on
managing these computing issues rather than their core business.
Thus, they move to service providers. Service providers such as
data centers serve their clients by sharing resources for
computing, storage etc. and maintaining all those.
Parallel and Distributed Computing: BOINC Grid Implementation PaperRodrigo Neves
Parallel and Distributed Computing: BOINC Grid Implementation Paper done while on the Parallel and Distributed System course of the Masters Degree on Electronics and Telecomunication Engeneering from University of Algarve
Iterative computations are at the core of the vast majority of data-intensive scientific computations. Recent advancements in data intensive computational fields are fueling a dramatic growth in number as well as usage of such data intensive iterative computations. The utility computing model introduced by cloud computing combined with the rich set of cloud infrastructure services offers a very viable environment for the scientists to perform data intensive computations. However, clouds by nature offer unique reliability and sustained performance challenges to large scale distributed computations necessitating computation frameworks specifically tailored for cloud characteristics to harness the power of clouds easily and effectively. My research focuses on identifying and developing user-friendly distributed parallel computation frameworks to facilitate the optimized efficient execution of iterative as well as non-iterative data-intensive computations in cloud environments, alongside the evaluation of heterogeneous cloud resources offering GPGPU resources in addition to CPU resources, for data-intensive iterative computations.
High Performance Parallel Computing with Clouds and Cloud Technologiesjaliyae
Infrastructure services (Infrastructure-as-a-service), provided by cloud vendors, allow any user to provision a large number of compute instances fairly easily. Whether leased from public clouds or allocated from private clouds, utilizing these virtual resources to perform data/compute intensive analyses requires employing different parallel runtimes to implement such applications. Among many parallelizable problems, most “pleasingly parallel” applications can be performed using MapReduce technologies such as Hadoop, CGL-MapReduce, and Dryad, in a fairly easy manner. However, many scientific applications, which have complex communication patterns, still require low latency communication mechanisms and rich set of communication constructs offered by runtimes such as MPI. In this paper, we first discuss large scale data analysis using different MapReduce implementations and then, we present a performance analysis of high performance parallel applications on virtualized resources.
Understand and Harness the Capabilities of Intel® Xeon Phi™ ProcessorsIntel® Software
The second-generation Intel® Xeon Phi™ processor offers new and enhanced features that provide significant performance gains in modernized code. For this lab, we pair these features with Intel® Software Development Products and methodologies to enable developers to gain insights on application behavior and to find opportunities to optimize parallelism, memory, and vectorization features.
Quantifying Overheads in Charm++ and HPX using Task BenchPatrick Diehl
Asynchronous Many-Task (AMT) runtime systems take advantage of multi-core architectures with light-weight threads, asynchronous executions, and smart scheduling. In this paper, we present the comparison of the AMT systems Charm++ and HPX with the main stream MPI, OpenMP, and MPI+OpenMP libraries using the Task Bench benchmarks. Charm++ is a parallel programming language based on C++, supporting stackless tasks as well as light-weight threads asynchronously along with an adaptive runtime system. HPX is a C++ library for concurrency and parallelism, exposing C++ standards conforming API. First, we analyze the commonalities, differences, and advantageous scenarios of Charm++ and HPX in detail. Further, to investigate the potential overheads introduced by the tasking systems of Charm++ and HPX, we utilize an existing parameterized benchmark, Task Bench, wherein 15 different programming systems were implemented, e.g., MPI, OpenMP, MPI + OpenMP, and extend Task Bench by adding HPX implementations. We quantify the overheads of Charm++, HPX, and the main stream libraries in different scenarios where a single task and multi-task are assigned to each core, respectively. We also investigate each system's scalability and the ability to hide the communication latency.
High Performance & High Throughput Computing - EUDAT Summer School (Giuseppe ...EUDAT
Giuseppe will present the differences between high-performance and high-throughput applications. High-throughput computing (HTC) refers to computations where individual tasks do not need to interact while running. It differs from High-performance (HPC) where frequent and rapid exchanges of intermediate results is required to perform the computations. HPC codes are based on tightly coupled MPI, OpenMP, GPGPU, and hybrid programs and require low latency interconnected nodes. HTC makes use of unreliable components distributing the work out to every node and collecting results at the end of all parallel tasks.
Visit: https://www.eudat.eu/eudat-summer-school
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.
Similar to Full introduction to_parallel_computing (20)
4. What is Parallel Computing?
A form of computation in which many calculations are
carried out simultaneously, operating on the principle
that large problems can often be divided into smaller
ones, which are then solved concurrently ("in parallel").
1
[Almasi and Gottlieb, 1989]
Problem
Task Problem
Task Task Task
Instructions
… … … …
CPU CPU CPU CPU
4
5. Pattern of Parallelism
Data parallelism [Quinn, 2003] 2
There are independent tasks applying the same
operation to different elements of a data set.
for i ← 0 to 99 do
a[i] = b[i] + c[i]
endfor
Functional Parallelism [Quinn, 2003] 2
There are independent tasks applying different
operations to different data elements.
a = 2, b=3
m = (a + b) / 2
n = a 2 + b2
5
7. Why use Parallel Computing?
Reduce computing time
More Processor
7
8. Why use Parallel Computing? (1)
Solve larger problems
More Memory
Problem
Task Problem
Task Task Task
Instructions
… … … …
RAM RAM RAM
RAM RAM
8
9. Parallel Computing Systems
• A single machine with multi-core processors
Process
Memory
C C C C
Multithreaded
C C C C
P P
Problem
Limits of a single machine (performance, available memory)
9
10. What is Cluster?
A group of linked computers, working together
closely so that in many respects they from a single
computer
To improve performance and/or availability over
that provided by a single computer 3
[Webopedia computer dictionary, 2007]
High-Performance High-Availability
10
12. Message-Passing model
The system is assumed to be a collection of processors,
each with its own local memory (Distributed memory
system)
A processor has direct access only to the instructions
and data stored in its local memory
An interconnection network supports message passing
between processors
MPI Standard
2
[Quinn, 2003] 12
13. Performance metrics
for parallel computing
• Speedup [Kumar et al., 1994] 4
How much performance gain is achieved
parallelizing a given application over a sequential
implementation
SP - speedup with p processors
TS
Sp = P Ts Tp Sp
TP
4 40 15 2.67
where
TS - a sequential execution time
P - a number of processors
TP - a parallel execution time
with p processors
13
15. Efficiency
A measure of processor utilization [Quinn, 2003] 2
EP - Efficiency with p processors
SP P Sp Ep
Ep =
P 4 2 0.5
8 3 0.375
In practice, speedup is less than p and efficiency is
between zero and one, depending on the degree of
effectiveness with which the processors are utilized
5
[Eijkhout, 2011]
15
16. Effective factors of
Parallel Performance
• Portion of computation [Quinn, 2003]
2
Computations that must be performed sequentially
Computations that can be performed in parallel
fs - Serial fraction of computation
fp - Parallel fraction of computation
TS TS 1
Sp = = =
TP fs(Ts) + fp(Ts) fs + fp
P P
TS fs fp fs(TS) fp(Ts)
100 10% 90% 10 90 16
17. Effective factors of
Parallel Performance (1)
• Parallel Overhead [Barney, 2011]
6
The amount of time required to coordinate
parallel tasks, as opposed to doing useful
work
o Task start-up time
o Synchronizations
o Data communications
o Task termination time
• Load balancing, etc.
17
19. Effective factors of
Parallel Performance (3)
Fixed Problem Size
Fixed
Sp = TS = TS
TP (fs)Ts + (1 – fs)Ts + Toverhead
P
19
20. Effective factors of
Parallel Performance (4)
Fixed P; Problem Size => Speedup
P
Sp = TS = 0
TS
0
TP (fs)Ts + (1 – fs)Ts + Toverhead
P
2D grid calculations 85 mins 85% 680 mins 97.84%
Serial fraction 15 mins 15% 15 mins 2.16%
20
21. Case Study
Hardware Configuration
Linux Cluster (4 compute nodes)
Detail of Compute node
o 2x Intel Xeon 2.80 GHz (Single core)
o 4 GB RAM
o Gigabit Ethernet
o CentOS 4.3
21
22. Case Study - CFD
Parallel Fluent Processing [Junhong, 2004] 7
Run Fluent solver on two or more CPUs
simultaneously to calculate a computational
fluid dynamics (CFD) job
22
24. Case Study – CFD (2)
Case Test #1 – Runtime
24
25. Case Study – CFD (3)
Case Test #1 – Speedup
25
26. Case Study – CFD (4)
Case Test #1 – Efficiency
26
27. Conclusion
Parallel computing help to save time of
computation and solve larger problems over that
provided by a single computer (sequential
computing)
To use parallel computers, then software is
developed with parallel programming model
Performance of parallel computing is measured
with speedup and efficiency
27
28. Reference
1. G.S. Almasi and A. Gottlieb. 1989. Highly Parallel Computing. The
Benjamin-Cummings publishers, Redwood City, CA.
2. M.J. Quinn. 2003. Parallel Programming in C with MPI and
OpenMP. The McGraw-Hill Companies, Inc. NY.
3. What is clustering?. Webopedia computer dictionary. Retrieved on
November 7, 2007.
4. V. Kumar, A. Grama, A. Gupta, and G. Karypis. 1994. Introduction
to parallel computing: design and analysis of parallel algorithms.
The Benjamin-Cummings publishers, Redwood City, CA.
5. V. Eijkhout. 2011. Introduction to Parallel Computing. Texas
Advanced Computing Center (TACC), The University of Texas at
Austin.
6. B. Barney. 2011. Introduction to Parallel Computing. Lawrence
Livermore National Laboratory.
7. Junhong, W. 2004. Parallel Fluent Processing. SVU/Academic
Computing, Computer Centre, National University of Singapore.
28
Editor's Notes
serial computation: To be run on a single computer having a single Central Processing Unit (CPU); A problem is broken into a discrete series of instructions. Instructions are executed one after another. Only one instruction may execute at any moment in time.
Multithreading as a widespread programming and execution model allows multiple threads to exist within the context of a single process. These threads share the process' resources but are able to execute independently. The threaded programming model provides developers with a useful abstraction of concurrent execution. However, perhaps the most interesting application of the technology is when it is applied to a single process to enable parallel execution on a multiprocessor system.-----------------------------------------------------------------------------Shared memory systems (SMPs, cc-NUMAs) have a single address space -----------------------------------------------------------OpenMP is the standard for shared memory programming (compiler directives)
Clusters vs. MPPs The key differences between a cluster and an MPP system are: In a cluster various components or layers can change relatively independently of each other, whereas components in MPP systems are much more tightly integrated. For example, a cluster administrator can choose to upgrade the interconnect, say from fast ethernet to gigabit ethernet, just by adding new network interface cards (NICs) and switches to the cluster. On the other hand, in most cases the administrator for an MPP system cannot do such upgrades without upgrading the whole machine. A cluster decouples the development of system software from innovations in underlying hardware. Cluster management tools and parallel programming libraries can be optimized independent of the changes in the node hardware itself. This results in more mature and reliable cluster middleware software as compared to the system software layer in an MPP class system, which requires at least a major rewrite with each generation of the system hardware. An MPP usually has a single system serial number used for software licensing and support tracking. Clusters and NOW have multiple serial numbers, one for each of their constituent nodes.
MPI is the standard for distributed memory programming (library of subprogram calls)------------------------------------------------------------------------------In computer hardware, shared memory refers to a (typically) large block of random access memory (RAM) that can be accessed by several different central processing units (CPUs) in a multiple-processor computer system.---------------------------------------------------------------------------------------------------------Shared memory systems (SMPs, cc-NUMAs) have a single address space Distributed memory systems have separate address spaces for each processor --------------------------------------------------------------------------------------------------Message Passing Interface (MPI) is a standardized and portable message-passing system designed by a group of researchers from academia and industry to function on a wide variety of parallel computers. The standard defines the syntax and semantics of a core of library routines useful to a wide range of users writing portable message-passing programs in Fortran 77 or the C programming language. Several well-tested and efficient implementations of MPI include some that are free and in the public domain. These fostered the development of a parallel software industry, and there encouraged development of portable and scalable large-scale parallel applications.MPI is a library specification for message-passing, proposed as a standard by a broadly based committee of vendors, implementors, and users. ------------------------------------------------------------------------------------------From a programming perspective, message passing implementations usually comprise a library of subroutines. Calls to these subroutines are imbedded in source code. The programmer is responsible for determining all parallelism. Historically, a variety of message passing libraries have been available since the 1980s. These implementations differed substantially from each other making it difficult for programmers to develop portable applications. In 1992, the MPI Forum was formed with the primary goal of establishing a standard interface for message passing implementations.