All the content is taken from Advance Computer Architecture book. Which (10.1.3 and 10.1.4)
This PPT covers the basics of Data-Parallel Model and Object-Oriented Model.
System Interconnect Architectures,Network Properties and Routing,Linear Array,
Ring and Chordal Ring,
Barrel Shifter,
Tree and Star,
Fat Tree,
Mesh and Torus,Dynamic InterConnection Networks,Dynamic bus ,Switch Modules
,Multistage Networks,Omega Network,Baseline Network,Crossbar Networks
INTRODUCTIONTO OPERATING SYSTEM
What is an Operating System?
Mainframe Systems
Desktop Systems
Multiprocessor Systems
Distributed Systems
Clustered System
Real -Time Systems
Handheld Systems
Computing Environments
System Interconnect Architectures,Network Properties and Routing,Linear Array,
Ring and Chordal Ring,
Barrel Shifter,
Tree and Star,
Fat Tree,
Mesh and Torus,Dynamic InterConnection Networks,Dynamic bus ,Switch Modules
,Multistage Networks,Omega Network,Baseline Network,Crossbar Networks
INTRODUCTIONTO OPERATING SYSTEM
What is an Operating System?
Mainframe Systems
Desktop Systems
Multiprocessor Systems
Distributed Systems
Clustered System
Real -Time Systems
Handheld Systems
Computing Environments
Presentation on Static Network Architecture for multi-programming and multi-processing. Architecture, Ring Architecture, Ring Chordal Architecture, Barrel Shifter Architecture, Fully Connected Architecture.
Presentation on Static Network Architecture for multi-programming and multi-processing. Architecture, Ring Architecture, Ring Chordal Architecture, Barrel Shifter Architecture, Fully Connected Architecture.
This chapter discusses various classification attributed to parallel architectures. It also introduces related parallel programming models and presents the actions of these models on parallel architectures. Notions such as Data parallelism Task parallelism, Tighty and Coupled system, UMA/NUMA, Multicore computing, Symmetric multiprocessing, Distributed Computing, Cluster computing, Shared memory without thread/Thread, etc..
Concurrency Programming in Java - 01 - Introduction to Concurrency ProgrammingSachintha Gunasena
This session discusses a basic high-level introduction to concurrency programming with Java which include:
programming basics, OOP concepts, concurrency, concurrent programming, parallel computing, concurrent vs parallel, why concurrency, real world example, terms, Moore's Law, Amdahl's Law, types of parallel computation, MIMD Variants, shared memory model, distributed memory model, client server model, scoop mechanism, scoop preview - a sequential program, in a concurrent setting - using scoop, programming then & now, sequential programming, concurrent programming,
Simulation of Heterogeneous Cloud InfrastructuresCloudLightning
During the last years, except from the traditional CPU based hardware servers, hardware accelerators are widely used in various HPC application areas. More specifically, Graphics Processing Units (GPUs), Many Integrated Cores (MICs) and Field-Programmable Gate Arrays (FPGAs) have shown a great potential in HPC and have been widely mobilised in supercomputing and in HPC-Clouds. This presentation focuses on the development of a cloud simulation framework that supports hardware accelerators. The design and implementation of the framework are also discussed.
This presentation was given by Dr. Konstantinos Giannoutakis (CERTH) at the CloudLightning Conference on 11th April 2017.
Machine Learning At Speed: Operationalizing ML For Real-Time Data StreamsLightbend
Audience: Architects, Data Scientists, Developers
Technical level: Introductory
From home intrusion detection, to self-driving cars, to keeping data center operations healthy, Machine Learning (ML) has become one of the hottest topics in software engineering today. While much of the focus has been on the actual creation of the algorithms used in ML, the less talked-about challenge is how to serve these models in production, often utilizing real-time streaming data.
The traditional approach to model serving is to treat the model as code, which means that ML implementation has to be continually adapted for model serving. As the amount of machine learning tools and techniques grows, the efficiency of such an approach is becoming more questionable. Additionally, machine learning and model serving are driven by very different quality of service requirements; while machine learning is typically batch, dealing with scalability and processing power, model serving is mostly concerned with performance and stability.
In this webinar with O’Reilly author and Lightbend Principal Architect, Boris Lublinsky, we will define an alternative approach to model serving, based on treating the model itself as data. Using popular frameworks like Akka Streams and Apache Flink, Boris will review how to implement this approach, explaining how it can help you:
* Achieve complete decoupling between the model implementation for machine learning and model serving, enforcing better standardization of your model serving implementation.
* Enable dynamic updates of the served model without having to restart the system.
* Utilize Tensorflow and PMML as model representation and their usage for building “real time updatable” model serving architecture.
This talk is given at AITAM, Tekkali. I have introduced developments in multi-core computers along with their architectural developments. Also, I have explained about high performance computing, where these are used. Also, I have introduced to OpenMP fundamentals with live practice sessions.
Slides from Strata+Hadoop Singapore 2016 presenting how Deep Learning can be scaled both vertically and horizontally, when to use CPUs and when to use GPUs.
Similar to Data Parallel and Object Oriented Model (20)
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
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Data file handling has been effectively used in the program.
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Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
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Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
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3. Part IV Software for Parallel Programming
Chapter 10). Parallel Models, Languages, and Compilers
Parallel Programming Models
10.1.1 Shared Variable Model
10.1.2 Message Passing Model
7. Programming Model
A collection of program abstractions providing a programmer a
simplified and transparent view of the computer
Hardware/software system.
Parallel programming models are specifically designed for
multiprocessors, multicomputer, or vector SIMD computers.
9. SIMD
• Parallelism is explicitly handled by hardware synchronization and flow
control.
• Choice of Data structure Figure courtesy: [2]
Main Focus
( local computations and data routing operation)
11. Single Instruction, Multiple Data (SIMD)
• Single instruction:All processing units execute the same
instruction at any given clock cycle
• Multiple data: Each processing unit can operate on a different data element
• Best suited for specialized problems characterized by a high degree of regularity, such as
image processing.
• Two varieties: Processor Arrays and Vector Pipelines
• Examples:
• Processor Arrays: Connection Machine CM-2, Maspar MP-1, MP-2
• Vector Pipelines: IBM 9000, Cray C90, Fujitsu VP, NEC SX-2, Hitachi S820
14. Data parallelism
• Challenge
• Matching of problem size with fixed machine size
• Ex. Partitioning of large arrays or matrixes in to 64 –
element segments.
SCIENTIST BE LIKE
15. Synhronization
• Data – parallel operation is done at
COMPILE TIME
Instead of
RUN TIME
Hardware synchronization: Enforced by control unit and LOCKSTEP execution.
16. Synchronous SIMD
• Lockstep fashion
• Do not have mutual exclusion or synchronization problem associated
with multiprocessors or multicomputer
• Inter-PE directly controlled by hardware
• Inter-PE data communication is also carried by lockstep
• Spatial Parallelism
17. Parallelism Example
• What is the latency and throughput if Ben uses parallelism?
• Spatial parallelism: Ben asks Allysa to help, using her own oven
• Temporal parallelism: Ben breaks the task into two stages: roll and
baking. He uses two trays. While the first batch is baking he rolls
the second batch, and so on.
18. Spatial Parallelism
Spatial
Parallelism
Roll
Bake
Ben 1 Ben 1
Alyssa 1 Alyssa 1
Ben 2 Ben 2
Alyssa 2 Alyssa 2
Time
0 5 10 15 20 25 30 35 40 45 50
Tray 1
Tray 2
Tray 3
Tray 4
Latency:
time to
first tray
Legend
Latency = ?
Throughput = ?
19. Spatial Parallelism
Spatial
Parallelism
Roll
Bake
Ben 1 Ben 1
Alyssa 1 Alyssa 1
Ben 2 Ben 2
Alyssa 2 Alyssa 2
Time
0 5 10 15 20 25 30 35 40 45 50
Tray 1
Tray 2
Tray 3
Tray 4
Latency:
time to
first tray
Legend
Latency = 5 + 15 = 20 minutes = 1/3 hour (same)
Throughput = 2 trays/ 1/3 hour = 6 trays/hour (doubled)
20. SIMD--- SCALAR v/s VECTOR
• Scalar are directly executed by the control unit.
• Vector are broadcast to all processing elements.
• (because vector operands are located at different location-PE)
21. Array Language Extensions
Array extensions in data parallel languages are represented by high-level
data types.
Enables the removal of some nested loops in the code.
global address space, which obviates the need for explicit data routing between PEs
In computer science, array programming refers to solutions which allow the
application of operations to an entire set of values at once. Such solutions are
commonly used in scientific and engineering settings. (source wiki ) Fortan 77
22. Compiler Support
• facilitate precise control of massively parallel hardware, and
enable incremental migration to data-parallel execution.
• Compiler-optimized control of SIMD machine hardware allows the
programmer to drive the PE array transparently. The compiler
must separate the program into scalar and parallel components
and integrate with the OS environment.
• array extensions to optimize data placement, minimize data
movement, and virtualize the dimensions of the PE array. The
compiler generates data-parallel machine code to perform
operations on arrays.
23. Array sectioning
• allows a programmer to reference a section or a region of a
multidimensional array.
• Array sections are designated by specifying a start index, a bound, and a
stride.
• Vector-valued subscripts arc often used to construct arrays from arbitrary
permutations of another array.
These expressions are vectors that map the desired elements into the target
array. They facilitate the implementation of gather and scatter operations on
a vector of indices.
24. • SPMD programs are a special class of SIMD programs which
emphasize medium-grain parallelism and synchronization at the
subprogram level rather than at the instruction level.
25. What was covered ?
• Lockstep operation
• SIMD (data parallel model)
• Synchronization
• Spatial Parallelism
• SIMD--- SCALAR v/s VECTOR
• Array Language ( Fortan 77 ) and extension
• Compiler support
27. Part IV Software for Parallel Programming
Chapter 10). Parallel Models, Languages, and Compilers
Parallel Programming Models
10.1.1 Shared Variable Model
10.1.2 Message Passing Model
Covered 10.1.3 Data- Parallel Model
10.1 .4 Object-Oriented Model
28. In this model
• In this model, objects are dynamically created and manipulated.
• Concurrent programming models are built up from low-level objects
such as processes, queues, and semaphores into high level objects
like monitors and program modules.
29. Concurrent Object
First
• increased use of interacting processes by individual users
Second
• workstation networks have become a cost-effective mechanism for
resource sharing and distributed problem solving
Third
• multiprocessor technology in several variants has advanced to the
point of providing supercomputing power at a fraction of the
traditional cost.
30. Program abstraction
• Program modularity and software reusability as is commonly
experienced with OOP
Objects
• Program entities which encapsulate data and operations into single
computational units.
31. An Actor Model
• Message passing is attached with semantics
• Create: Creating an actor from a behavior description and a set of
parametric.
• Send-to: Sending a message to another actor.
• Become: An actor replacing its own behavior by a new behavior
32. Parallelism in COOP
• Pipeline concurrency involves the overlapped enumeration of
successive solutions and concurrent testing of the solutions as they
emerge from an evaluation pipeline.
• Divide-and-conquer concurrency
Example
A prime-number generation pipeline in
Integer numbers are generated and successively tested for divisibility
by previously generated primes in a linear pipeline of primes.
33. Example 10.2 Concurrency in object-oriented
programming
• Integer numbers are generated and successively tested for divisibility
by previously generated primes in a linear pipeline of primes.
34. • Multiplication of a list of numbers [10, 7, -2, 3, 4, -11, -3] using a
divide and-conquer approach
Figure courtesy: ACA a book by Jotwani