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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Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
This document discusses multiprocessor architecture types and limitations. It describes tightly coupled and loosely coupled multiprocessing systems. Tightly coupled systems have shared memory that all CPUs can access, while loosely coupled systems have each CPU connected through message passing without shared memory. Examples given are symmetric multiprocessing (SMP) and Beowulf clusters. Interconnection structures like common buses, multiport memory, and crossbar switches are also outlined. The advantages of multiprocessing include improved performance from parallel processing, increased reliability, and higher throughput.
There are three main types of shared memory architectures: physically shared memory, virtual shared memory, and cache-only memory access (COMA). Virtual shared memory, also called distributed shared memory, logically shares memory across processors but physically distributes it. This can cause non-uniform memory access times and requires solutions for cache coherency and data consistency. Cache-coherent non-uniform memory access (CC-NUMA) machines combine the approaches of NUMA and COMA to provide a unified memory addressing scheme while improving performance. Key challenges for shared memory architectures include scalability issues due to memory contention and latency.
Distributed shared memory (DSM) allows nodes in a cluster to access shared memory across the cluster in addition to each node's private memory. DSM uses a software memory manager on each node to map local memory into a virtual shared memory space. It consists of nodes connected by high-speed communication and each node contains components associated with the DSM system. Algorithms for implementing DSM deal with distributing shared data across nodes to minimize access latency while maintaining data coherence with minimal overhead.
program partitioning and scheduling IN Advanced Computer ArchitecturePankaj Kumar Jain
Advanced Computer Architecture,Program Partitioning and Scheduling,Program Partitioning & Scheduling,Latency,Levels of Parallelism,Loop-level Parallelism,Subprogram-level Parallelism,Job or Program-Level Parallelism,Communication Latency,Grain Packing and Scheduling,Program Graphs and Packing
The document provides an introduction to distributed systems, defining them as a collection of independent computers that communicate over a network to act as a single coherent system. It discusses the motivation for and characteristics of distributed systems, including concurrency, lack of a global clock, and independence of failures. Architectural categories of distributed systems include tightly coupled and loosely coupled, with examples given of different types of distributed systems such as database management systems, ATM networks, and the internet.
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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@ Appreciate my work:
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Thank-you !
Parallel computing is computing architecture paradigm ., in which processing required to solve a problem is done in more than one processor parallel way.
This document discusses multiprocessor architecture types and limitations. It describes tightly coupled and loosely coupled multiprocessing systems. Tightly coupled systems have shared memory that all CPUs can access, while loosely coupled systems have each CPU connected through message passing without shared memory. Examples given are symmetric multiprocessing (SMP) and Beowulf clusters. Interconnection structures like common buses, multiport memory, and crossbar switches are also outlined. The advantages of multiprocessing include improved performance from parallel processing, increased reliability, and higher throughput.
There are three main types of shared memory architectures: physically shared memory, virtual shared memory, and cache-only memory access (COMA). Virtual shared memory, also called distributed shared memory, logically shares memory across processors but physically distributes it. This can cause non-uniform memory access times and requires solutions for cache coherency and data consistency. Cache-coherent non-uniform memory access (CC-NUMA) machines combine the approaches of NUMA and COMA to provide a unified memory addressing scheme while improving performance. Key challenges for shared memory architectures include scalability issues due to memory contention and latency.
Distributed shared memory (DSM) allows nodes in a cluster to access shared memory across the cluster in addition to each node's private memory. DSM uses a software memory manager on each node to map local memory into a virtual shared memory space. It consists of nodes connected by high-speed communication and each node contains components associated with the DSM system. Algorithms for implementing DSM deal with distributing shared data across nodes to minimize access latency while maintaining data coherence with minimal overhead.
program partitioning and scheduling IN Advanced Computer ArchitecturePankaj Kumar Jain
Advanced Computer Architecture,Program Partitioning and Scheduling,Program Partitioning & Scheduling,Latency,Levels of Parallelism,Loop-level Parallelism,Subprogram-level Parallelism,Job or Program-Level Parallelism,Communication Latency,Grain Packing and Scheduling,Program Graphs and Packing
The document provides an introduction to distributed systems, defining them as a collection of independent computers that communicate over a network to act as a single coherent system. It discusses the motivation for and characteristics of distributed systems, including concurrency, lack of a global clock, and independence of failures. Architectural categories of distributed systems include tightly coupled and loosely coupled, with examples given of different types of distributed systems such as database management systems, ATM networks, and the internet.
The document discusses different types of parallel computer architectures, including shared-memory multiprocessors. It describes taxonomy of parallel computers including SISD, SIMD, MISD, and MIMD models. For shared-memory multiprocessors, it outlines consistency models including strict, sequential, processor, weak and release consistency. It also discusses UMA and NUMA architectures, cache coherence protocols like MESI, and examples of multiprocessors using crossbar switches or multistage networks.
This document discusses centralized shared-memory architectures and cache coherence protocols. It begins by explaining how multiple processors can share memory through a shared bus and cached data. It then discusses the cache coherence problem that arises when caches contain replicated data. Write invalidate is introduced as the most common coherence protocol, where a write invalidates other caches' copies of the block. The implementation of write invalidate protocols with snooping and directory approaches is covered, focusing on supporting write-back caches through tracking shared state and bus snooping.
Parallel and distributed computing allows problems to be broken into discrete parts that can be solved simultaneously. This approach utilizes multiple processors that work concurrently on different parts of the problem. There are several types of parallel architectures depending on how instructions and data are distributed across processors. Shared memory systems give all processors access to a common memory space while distributed memory assigns private memory to each processor requiring explicit data transfer. Large-scale systems may combine these approaches into hybrid designs. Distributed systems extend parallelism across a network and provide users with a single, integrated view of geographically dispersed resources and computers. Key challenges for distributed systems include transparency, scalability, fault tolerance and concurrency.
Memory system, and not processor speed, is often the bottleneck for many applications.
Memory system performance is largely captured by two parameters, latency and bandwidth.
Latency is the time from the issue of a memory request to the time the data is available at the processor.
Bandwidth is the rate at which data can be pumped to the processor by the memory system.
The document discusses parallel programming using MPI (Message Passing Interface). It introduces MPI as a standard for message passing between processes. It describes how to set up a basic parallel computing environment using a cluster of networked computers. It provides examples of using MPI functions to implement parallel algorithms, including point-to-point and collective communication like broadcast, gather, and scatter.
This document discusses parallelism and its goals of increasing computational speed and throughput. It describes two types of parallelism: instruction level parallelism and processor level parallelism. Instruction level parallelism techniques include pipelining and superscalar processing to allow multiple instructions to execute simultaneously. Processor level parallelism involves multiple independent processors working concurrently through approaches like array computers and multi-processors.
Parallel computing involves solving computational problems simultaneously using multiple processors. It can save time and money compared to serial computing and allow larger problems to be solved. Parallel programs break problems into discrete parts that can be solved concurrently on different CPUs. Shared memory parallel computers allow all processors to access a global address space, while distributed memory systems require communication between separate processor memories. Hybrid systems combine shared and distributed memory architectures.
This document discusses different file models and methods for accessing files. It describes unstructured and structured file models, as well as mutable and immutable files. It also covers remote file access using remote service and data caching models. Finally, it discusses different units of data transfer for file access, including file-level, block-level, byte-level, and record-level transfer models.
This document discusses different distributed computing system (DCS) models:
1. The minicomputer model consists of a few minicomputers with remote access allowing resource sharing.
2. The workstation model consists of independent workstations scattered throughout a building where users log onto their home workstation.
3. The workstation-server model includes minicomputers, diskless and diskful workstations, and centralized services like databases and printing.
It provides an overview of the key characteristics and advantages of different DCS models.
This document discusses multiprocessor computer systems. It begins by defining a multiprocessor system as having two or more CPUs connected to a shared memory and I/O devices. Multiprocessors are classified as MIMD systems. They provide benefits like improved performance over single CPU systems for tasks like multi-user/multi-tasking applications. Multiprocessors are further classified as tightly-coupled or loosely-coupled based on shared vs distributed memory. Common interconnection structures discussed include bus, multport memory, crossbar switch, and hypercube networks.
This document discusses parallel processing concepts including:
1. Parallel computing involves simultaneously using multiple processing elements to solve problems faster than a single processor. Common parallel platforms include shared-memory and message-passing architectures.
2. Key considerations for parallel platforms include the control structure for specifying parallel tasks, communication models, and physical organization including interconnection networks.
3. Scalable design principles for parallel systems include avoiding single points of failure, pushing work away from the core, and designing for maintenance and automation. Common parallel architectures include N-wide superscalar, which can dispatch N instructions per cycle, and multi-core which places multiple cores on a single processor socket.
The document discusses cache coherence in multiprocessor systems. It describes the cache coherence problem that can arise when multiple processors have caches and can access shared memory. It then summarizes two primary hardware solutions: directory protocols which maintain information about which caches hold which memory lines; and snoopy cache protocols where cache controllers monitor bus traffic to maintain coherence without a directory. Finally it mentions a software-based solution relying on compiler analysis and operating system support.
A Distributed Shared Memory (DSM) system provides a logical abstraction of shared memory built using interconnected nodes with distributed physical memories. There are hardware, software, and hybrid DSM approaches. DSM offers simple abstraction, improved portability, potential performance gains, large unified memory space, and better performance than message passing in some applications. Consistency protocols ensure shared data coherency across distributed memories according to the memory consistency model.
The document discusses the memory hierarchy in computers. It describes the different levels of memory from fastest to slowest as register memory, cache memory, main memory (RAM and ROM), and auxiliary memory (magnetic tapes, hard disks, etc.). The main memory directly communicates with the CPU while the auxiliary memory provides backup storage and needs to transfer data to main memory to be accessed by the CPU. A cache memory is also used to increase processing speed.
This document discusses vector processing and multiprocessor principles. It explains that vector processing performs operations on vectors to gain speedups of 10-20x over scalar processing. Multiprocessor systems use two or more CPUs for advantages like reduced costs, increased reliability and throughput. They can implement techniques like multitasking, multithreading and multiprogramming to execute multiple tasks simultaneously.
Data Parallel and Object Oriented ModelNikhil Sharma
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.
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
Operating system 31 multiple processor schedulingVaibhav Khanna
CPU scheduling more complex when multiple CPUs are available
Homogeneous processors within a multiprocessor
Asymmetric multiprocessing – only one processor accesses the system data structures, alleviating the need for data sharing
Symmetric multiprocessing (SMP) – each processor is self-scheduling, all processes in common ready queue, or each has its own private queue of ready processes
Currently, most common
Processor affinity – process has affinity for processor on which it is currently running
soft affinity
hard affinity
Variations including processor sets
The document discusses parallel computing and different types of parallel architectures. It begins with an overview of parallelism and how problems can be solved simultaneously using multiple processors. It then describes different classifications of parallel architectures including SISD, SIMD, MIMD, MISD, and vector processors. Specific examples like symmetric multiprocessors and hardware multithreading are also summarized. The document provides information on parallel computing concepts at a high level.
This document provides an introduction to high performance computer architecture and multiprocessors. It discusses how initial improvements in computer performance came from innovative manufacturing techniques and exploitation of instruction level parallelism (ILP). More recently, exploiting thread and process level parallelism across multiple processors has become a focus. The key types of multiprocessor architectures discussed are symmetric multiprocessors (SMPs) and distributed memory computers which use message passing. SMPs connect multiple processors to a shared memory using a bus, while distributed memory computers require explicit message passing between separate processor memories.
The document discusses different types of parallel computer architectures, including shared-memory multiprocessors. It describes taxonomy of parallel computers including SISD, SIMD, MISD, and MIMD models. For shared-memory multiprocessors, it outlines consistency models including strict, sequential, processor, weak and release consistency. It also discusses UMA and NUMA architectures, cache coherence protocols like MESI, and examples of multiprocessors using crossbar switches or multistage networks.
This document discusses centralized shared-memory architectures and cache coherence protocols. It begins by explaining how multiple processors can share memory through a shared bus and cached data. It then discusses the cache coherence problem that arises when caches contain replicated data. Write invalidate is introduced as the most common coherence protocol, where a write invalidates other caches' copies of the block. The implementation of write invalidate protocols with snooping and directory approaches is covered, focusing on supporting write-back caches through tracking shared state and bus snooping.
Parallel and distributed computing allows problems to be broken into discrete parts that can be solved simultaneously. This approach utilizes multiple processors that work concurrently on different parts of the problem. There are several types of parallel architectures depending on how instructions and data are distributed across processors. Shared memory systems give all processors access to a common memory space while distributed memory assigns private memory to each processor requiring explicit data transfer. Large-scale systems may combine these approaches into hybrid designs. Distributed systems extend parallelism across a network and provide users with a single, integrated view of geographically dispersed resources and computers. Key challenges for distributed systems include transparency, scalability, fault tolerance and concurrency.
Memory system, and not processor speed, is often the bottleneck for many applications.
Memory system performance is largely captured by two parameters, latency and bandwidth.
Latency is the time from the issue of a memory request to the time the data is available at the processor.
Bandwidth is the rate at which data can be pumped to the processor by the memory system.
The document discusses parallel programming using MPI (Message Passing Interface). It introduces MPI as a standard for message passing between processes. It describes how to set up a basic parallel computing environment using a cluster of networked computers. It provides examples of using MPI functions to implement parallel algorithms, including point-to-point and collective communication like broadcast, gather, and scatter.
This document discusses parallelism and its goals of increasing computational speed and throughput. It describes two types of parallelism: instruction level parallelism and processor level parallelism. Instruction level parallelism techniques include pipelining and superscalar processing to allow multiple instructions to execute simultaneously. Processor level parallelism involves multiple independent processors working concurrently through approaches like array computers and multi-processors.
Parallel computing involves solving computational problems simultaneously using multiple processors. It can save time and money compared to serial computing and allow larger problems to be solved. Parallel programs break problems into discrete parts that can be solved concurrently on different CPUs. Shared memory parallel computers allow all processors to access a global address space, while distributed memory systems require communication between separate processor memories. Hybrid systems combine shared and distributed memory architectures.
This document discusses different file models and methods for accessing files. It describes unstructured and structured file models, as well as mutable and immutable files. It also covers remote file access using remote service and data caching models. Finally, it discusses different units of data transfer for file access, including file-level, block-level, byte-level, and record-level transfer models.
This document discusses different distributed computing system (DCS) models:
1. The minicomputer model consists of a few minicomputers with remote access allowing resource sharing.
2. The workstation model consists of independent workstations scattered throughout a building where users log onto their home workstation.
3. The workstation-server model includes minicomputers, diskless and diskful workstations, and centralized services like databases and printing.
It provides an overview of the key characteristics and advantages of different DCS models.
This document discusses multiprocessor computer systems. It begins by defining a multiprocessor system as having two or more CPUs connected to a shared memory and I/O devices. Multiprocessors are classified as MIMD systems. They provide benefits like improved performance over single CPU systems for tasks like multi-user/multi-tasking applications. Multiprocessors are further classified as tightly-coupled or loosely-coupled based on shared vs distributed memory. Common interconnection structures discussed include bus, multport memory, crossbar switch, and hypercube networks.
This document discusses parallel processing concepts including:
1. Parallel computing involves simultaneously using multiple processing elements to solve problems faster than a single processor. Common parallel platforms include shared-memory and message-passing architectures.
2. Key considerations for parallel platforms include the control structure for specifying parallel tasks, communication models, and physical organization including interconnection networks.
3. Scalable design principles for parallel systems include avoiding single points of failure, pushing work away from the core, and designing for maintenance and automation. Common parallel architectures include N-wide superscalar, which can dispatch N instructions per cycle, and multi-core which places multiple cores on a single processor socket.
The document discusses cache coherence in multiprocessor systems. It describes the cache coherence problem that can arise when multiple processors have caches and can access shared memory. It then summarizes two primary hardware solutions: directory protocols which maintain information about which caches hold which memory lines; and snoopy cache protocols where cache controllers monitor bus traffic to maintain coherence without a directory. Finally it mentions a software-based solution relying on compiler analysis and operating system support.
A Distributed Shared Memory (DSM) system provides a logical abstraction of shared memory built using interconnected nodes with distributed physical memories. There are hardware, software, and hybrid DSM approaches. DSM offers simple abstraction, improved portability, potential performance gains, large unified memory space, and better performance than message passing in some applications. Consistency protocols ensure shared data coherency across distributed memories according to the memory consistency model.
The document discusses the memory hierarchy in computers. It describes the different levels of memory from fastest to slowest as register memory, cache memory, main memory (RAM and ROM), and auxiliary memory (magnetic tapes, hard disks, etc.). The main memory directly communicates with the CPU while the auxiliary memory provides backup storage and needs to transfer data to main memory to be accessed by the CPU. A cache memory is also used to increase processing speed.
This document discusses vector processing and multiprocessor principles. It explains that vector processing performs operations on vectors to gain speedups of 10-20x over scalar processing. Multiprocessor systems use two or more CPUs for advantages like reduced costs, increased reliability and throughput. They can implement techniques like multitasking, multithreading and multiprogramming to execute multiple tasks simultaneously.
Data Parallel and Object Oriented ModelNikhil Sharma
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.
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
Operating system 31 multiple processor schedulingVaibhav Khanna
CPU scheduling more complex when multiple CPUs are available
Homogeneous processors within a multiprocessor
Asymmetric multiprocessing – only one processor accesses the system data structures, alleviating the need for data sharing
Symmetric multiprocessing (SMP) – each processor is self-scheduling, all processes in common ready queue, or each has its own private queue of ready processes
Currently, most common
Processor affinity – process has affinity for processor on which it is currently running
soft affinity
hard affinity
Variations including processor sets
The document discusses parallel computing and different types of parallel architectures. It begins with an overview of parallelism and how problems can be solved simultaneously using multiple processors. It then describes different classifications of parallel architectures including SISD, SIMD, MIMD, MISD, and vector processors. Specific examples like symmetric multiprocessors and hardware multithreading are also summarized. The document provides information on parallel computing concepts at a high level.
This document provides an introduction to high performance computer architecture and multiprocessors. It discusses how initial improvements in computer performance came from innovative manufacturing techniques and exploitation of instruction level parallelism (ILP). More recently, exploiting thread and process level parallelism across multiple processors has become a focus. The key types of multiprocessor architectures discussed are symmetric multiprocessors (SMPs) and distributed memory computers which use message passing. SMPs connect multiple processors to a shared memory using a bus, while distributed memory computers require explicit message passing between separate processor memories.
Computer system Architecture. This PPT is based on computer systemmohantysikun0
This document discusses thread and process-level parallelism. It begins by introducing how improvements to computer performance initially came from manufacturing techniques and exploitation of instruction-level parallelism (ILP), but that ILP is now fully exploited. It states that the way to achieve higher performance now is through exploiting parallelism across multiple processes or threads. It provides examples of how individual transactions in a banking application could be executed in parallel.
Term paper of cse(211) avdhesh sharma c1801 a24 regd 10802037Upendra Sengar
This document is a term paper on shared memory MIMD (Multiple Instruction Multiple Data) computer architectures. It discusses the goals of MIMD architectures in allowing independent processors to operate concurrently on separate data streams. It then describes different types of shared memory MIMD architectures, including bus-based, extended, and hierarchical approaches. It also briefly introduces distributed memory MIMD architectures and discusses hypercube and mesh interconnection networks.
The document provides information on different types of computer system architectures including SISD, SIMD, MIMD, and MISD. It discusses the key characteristics of each architecture such as SISD involving a single processor executing a single instruction stream on data from a single memory. SIMD involves multiple processors executing the same instruction on multiple data streams simultaneously. MIMD involves multiple processors executing different instruction streams on different data simultaneously. Pipelining is described as a technique used to increase instruction throughput by splitting instruction processing into independent stages.
Flynn's taxonomy classifies computer architectures based on the number of instruction and data streams. The main categories are:
1) SISD - Single instruction, single data stream (von Neumann architecture)
2) SIMD - Single instruction, multiple data streams (vector/MMX processors)
3) MIMD - Multiple instruction, multiple data streams (most multiprocessors including multi-core)
Multiprocessor architectures can be organized as shared memory (SMP/UMA) or distributed memory (message passing/DSM). Shared memory allows automatic sharing but can have memory contention issues, while distributed memory requires explicit communication but scales better. Achieving high parallel performance depends on minimizing sequential
PGAS is a parallel programming model that aims to improve programmer productivity while still achieving high performance. It assumes a global memory address space that is logically partitioned, with each process or thread having a portion of memory local to it. Two languages that use this model are Chapel and X10. PGAS combines aspects of shared memory and distributed memory models - it allows data to be accessed globally like shared memory but exploits data locality like distributed memory. While it hides communication details, it does not eliminate communication latency. PGAS seeks to balance ease of programming with scalability.
Distributed system lectures
Engineering + education purpose
This series of lectures was prepared for the fourth class of computer engineering / Baghdad/ Iraq.
This series is not completed yet, it is just a few lectures in the object.
Forgive me for anything wrong by mistake, I wish you can profit from these lectures
My regard
Marwa Moutaz/ M.Sc. studies of Communication Engineering / University of Technology/ Bagdad / Iraq.
Unit IV discusses parallelism and parallel processing architectures. It introduces Flynn's classifications of parallel systems as SISD, MIMD, SIMD, and SPMD. Hardware approaches to parallelism include multicore processors, shared memory multiprocessors, and message-passing systems like clusters, GPUs, and warehouse-scale computers. The goals of parallelism are to increase computational speed and throughput by processing data concurrently across multiple processors.
The document discusses the importance and applications of high performance computing (HPC). It provides examples of when HPC is needed, such as to perform time-consuming operations more quickly or handle high volumes of data/transactions. It also outlines what HPC studies, including hardware components like computer architecture and networks, as well as software elements like programming paradigms and languages. Additionally, it notes the international competition around developing exascale supercomputers and some of the research areas that utilize HPC, such as finance, weather forecasting, and health care applications involving large datasets.
Software Design Practices for Large-Scale AutomationHao Xu
Design practices for large-scale, high-performance, distributed system for complex algorithms such as graph, optimization, prediction, and machine learning etc.
This document discusses different types of parallel computing architectures including vector architectures, SIMD instruction set extensions for multimedia, and graphics processing units (GPUs). It compares vector architectures to GPUs and multimedia SIMD computers to GPUs. It also covers loop level parallelism and techniques for finding data dependencies, such as using the greatest common divisor test.
An explicitly parallel program must specify concurrency and interaction between concurrent subtasks.
The former is sometimes also referred to as the control structure and the latter as the communication model.
This document discusses various models of parallel computer architectures. It begins with an overview of Flynn's taxonomy, which classifies computer systems based on the number of instruction and data streams. The main categories are SISD, SIMD, MIMD, and MISD. It then covers parallel computer models in more detail, including shared-memory multiprocessors, distributed-memory multicomputers, classifications based on interconnection networks and parallelism. It provides examples of different parallel architectures and references papers on advanced computer architecture and parallel processing.
The document discusses grid computing and provides examples. It begins with an introduction to supercomputers and provides Param Padma as an example. It then defines grid computing, discussing its evolution and advantages over supercomputers. Design considerations for grid computing include assigning work randomly to nodes to check for accurate results due to lack of central control. Implementation involves using middleware like BOINC and Alchemi, which are described. The document outlines service-oriented grid architecture and challenges. It provides examples of grid initiatives worldwide like TeraGrid in the US and Garuda in India.
The document discusses various models of parallel and distributed computing including symmetric multiprocessing (SMP), cluster computing, distributed computing, grid computing, and cloud computing. It provides definitions and examples of each model. It also covers parallel processing techniques like vector processing and pipelined processing, and differences between shared memory and distributed memory MIMD (multiple instruction multiple data) architectures.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Embedded machine learning-based road conditions and driving behavior monitoring
distributed memory architecture/ Non Shared MIMD Architecture
1. Distributed Memory Architecture
MS(CS) - I
Hafsa Habib
Syeda Haseeba Khanam
Amber Azhar
Zainab Khalid
Lahore College for Women University
Department of Computer Science
2. Content
● MIMD processor classification
● Distributed MIMD architecture
○ Basic difference between DM-MIMD and SM-MIMD
● Communication Techniques of DM-MIMD
● Major classification of DM-MIMD
○ NUMA
○ MPP
○ Cluster
● Pros and Cons of DM-MIMD over SM-MIMD architecture
○ Scalability
○ Issues in scalability
2
5. Non Shared MIMD Architecture
● Also called Distributed Memory MIMD or Message Passing MIMD
Computers or Loosely coupled MIMD
● Processors have their own memory local memory
○ Memory address for one processor does not map on other processors
○ No concept of global address space
● Each processor operates independently because of its own local memory
○ Changes in one processor’s local memory has no effect on other
processor’s local memory
○ Therefor cache synchronization and cache coherency does not apply.
● Inter Process Communication is done by Message Passing.
5
7. DM-MIMD vs SM-MIMD
DM-MIMD
● Private physical address space
for each processor
● Data must be explicitly assigned
to the private address space
● Communication/synchronization
via network by Message Passing
● Concept of cache coherency
does not apply because no global
address space
SM-MIMD
● Global address space shared by
all
● Data is implicitly assigned to the
address space.
● Cooperate by reading/writing
same shared variable
● Communication through BUS
● Concept of cache coherency
applies due to shared Global
address space 7
8. Content
● MIMD processor classification
● Distributed MIMD architecture
○ Basic difference between DM-MIMD and SM-MIMD
● Communication Technique
● Major classification of DM-MIMD
○ NUMA
○ MPP
○ Cluster
● Pros and Cons of DM-MIMD over SM-MIMD architecture
○ Scalability
○ Issues in scalability
8
10. Communication in DM
Architecture
● Require a communication
NETWORK to connect inter
processor memory.
● Communication and
Synchronization is done through
Message Passing Model.
● Processor share data by explicitly
send and receive information.
● Coordination is built into message
passing primitives
○ message SEND and message
RECEIVE
10
11. Why DM-
Architecture use Message
Passing Model?
In Distributed memory architecture there is no
global memory so it is necessary to move data
from one local memory to another by means of
message passing.
11
12. Message Passing Model
● Communication via
Send/Receive
○ Through Interconnection
Network
● Data is packed into larger
packets
● Send sends message to
another destination processor
● Receive indicates that a
processor is ready to receive a
message; message from
another source processor
12
13. Message Passing Model (cont’d)
● When a process interacts with another, two requirements have to be satisfied.
○ Synchronization and Communication
● Synchronization in message passing model is either asynchronous or
synchronous
○ If Asynchronous , it means no acknowledgement is required at both
ends(receiver and sender)
■ Sender and receiver don’t wait for each other and can carry on their
own computations while transfer of messages is being done.
○ If synchronous, Acknowledgement is required.
■ Both processors have to wait for each other while transferring the
message. (one blocks until the second is ready)
13
15. Pros and Cons of Message Passing Model
● The advantage for programmers is that
communication is explicit, so there are
fewer “performance surprises” than with
the implicit communication in cache-
coherent SMPs.
● Synchronization is naturally associated
with sending messages, reducing the
possibility for errors introduced by
incorrect synchronization
● Much easier for hardware designers to
design
● Message sending and receiving is much
slower
● It's harder to port a sequential program
to a message passing multiprocessor .
Pros Cons
15
17. Sr.No. Difference Distributed Memory
Architecture
Shared Memory
Architecture
1. Explicit Communication/Implicit
Communication
Explicit via Messages Implicit via Memory
Operations
2. Who is Responsible for carrying
communication task?
Programmer is
responsible to send
and receive data
Sending and receiving
is automatic.
System is Responsible
for setting data in
cache. Programmer
just load from
memory and store to
memory.
3. Synchronization Automatic Can be Achieved using
different mechanism
4. Protocols Fully under
programmer control
Hidden within the
system
17
18. Content
● MIMD processor classification
● Distributed MIMD architecture
○ Basic difference between DM-MIMD and SM-MIMD
● Communication Techniques of DM-MIMD
● Major classification of DM-MIMD
○ NUMA
○ MPP
○ Cluster
● Pros and Cons of DM-MIMD over SM-MIMD architecture
○ Scalability
○ Issues in scalability
18
22. NUMA (Non-Uniform memory Access)
● NUMA is a computer memory design
used in multiprocessing, where the
memory access time depends on the
memory location relative to the
processor.
● Under NUMA, a processor can access its
own local memory faster than non-local
memory (memory local to another
processor or memory shared between
processors).
● The benefits of NUMA are limited to
particular workloads, notably on servers
where the data is often associated
strongly with certain tasks or users,
● There are two morals to this performance
story.
● The first is that even a single 32-bit , but
already commonplace, processor is
starting to push the limits of standard
memory performance.
● The second is that even conventional
memory types differences play a role in
overall system performance. So it should
come as no surprise that NUMA support
is now in server operating systems. e.g
Microsoft’s Windows Server 2003 and in
Linux 2.6 kernel.
22
26. What is a Cluster
● Network of independent computers
○ Each has private memory and OS
○ Connected using I/O system
E.g., Ethernet/switch, Internet
● Independent Computers in a cluster are called Node
○ Master and computing Nodes
● Cluster Middleware is required
○ Message Passing Interface
● Node management is to be considered
● Appear as a single system to user
26
27. Clusters
Clusters split problem in smaller tasks
that are executed concurrently
Why?
● Absolute physical limits of
hardware components
● Economical reasons – more
complex = more expensive
● Performance limits – double
frequency <> double performance
● Large applications – demand too
much memory & time
Advantages:
Increasing speed & optimizing
resources utilization.greatly
independent of hardware
Disadvantages:
Complex programming models –
difficult development
Applications
Suitable for applications with
independent tasks
SuperComputers ,Web
servers, databases, simulations,
27
29. Clusters vs MPP
Similar to MPPs
● Commodity processor and memory
○ Processor performance must be maximized
● Memory Hierarchy includes remote memory
○ Non Uniform Memory Access
● No shared memory - message passing
29
30. Clusters vs MPPs
Clusters
● In a cluster, each machine is largely
independent of the others in terms
of memory, disk, etc.
● They are interconnected using
some variation on normal
networking.
● The cluster exists mostly in the
mind of the programmer and how
s/he chooses to distribute the
work.
● Best to use in servers with multiple
independent tasks.
MPPs
● In a Massively Parallel Processor,
there really is only one machine
with thousands of CPUs tightly
● Interconnected with I/O
subsystem.
● MPPs have exotic memory
architectures to allow extremely
high speed exchange of
intermediate results with
neighboring processors.
● MPPs are of use only on algorithms
that are embarrassingly parallel . 30
31. Content
● MIMD processor classification
● Distributed MIMD architecture
○ Basic difference between DM-MIMD and SM-MIMD
● Communication Technique
● Major classification of DM-MIMD
○ NUMA
○ MPP
○ Cluster
● Pros and Cons of DM-MIMD over SM-MIMD architecture
○ Issues in DM Architecture
○ Scalability
31
33. Pros of DM-MIMD over SM-MIMD
DM-MIMD
● Memory is scalable with the
number of processors.Increase the
number of processors and the size
of memory increases
proportionately.
● Each processor can rapidly access
its own memory without
interference and without overhead
with trying to maintain global cache
concurrency.
● Cost effectiveness: can use
commodity, off-the-shelf
processors and networking.
SM-MIMD
● Lack of scalability between
memory and CPUs: Adding more
CPUs can geometrically increases
traffic on the shared memory CPU
path,and geometrically increase
traffic associated with cache
memory management.
● Expense:it becomes increasingly
difficult and expensive to design
and produce shared memory
machines with ever increasing
number of processors.
33
34. Cons of DM-MIMD over SM-MIMD
DM-MIMD
● Non uniform memory access
times-data residing on a
remote node takes longer to
access than local data.
● The programmer is
responsible for many of the
details associated with data
communication processors .
SM-MIMD
● Data sharing between tasks
is both fast and uniform due
to the proximity of memory
to CPUs.
● Global address space
provides a user-friendly
programming perspective to
memory.
34
35. Issues of DM Architecture
Latency and Bandwidth for accessing distributed memory is the main memory
performance issues:
● Efficiency in parallel processing is usually related to ratio of time for calculation vs
time for communication, the higher the ratio the higher the performance.
● Problem is more even severe when access to distributed memory is needed,since
there is an extra level in the memory hierarchy,with latency and bandwidth that can
be very slower than local memory access.
35
36. Scalability and its issues
A scalable architecture is an architecture that can scale up to meet increased work loads. In other
words, if the workload all of a sudden exceeds the capacity of your existing software + hardware
combination, you can scale up the system (software + hardware) to meet the increased workload.
Scalability to more processor is the key issue
● Access times to “distinct processors should not be very much slower than
access to “nearby” processors since non-local and collective (all-to-all)
communication is important for many programs.This can be a problem for
large parallel computers (hundreds or thousands of processors). Many
different approaches to network topology and switching have been tried in
attempting to alleviate this program.
36
DM: Protocols are complex to programmer causing communication to be treated as an I/O call.
SM: Communication can be close to hardware because of shared bus system and if we modify our shared memory’s hardware then communication will be fast
Commodity computing involves the use of large numbers of already-available computing components for parallel computing, to get the greatest amount of useful computation at low cost
However, if you have such a problem, then an MPP can be shockingly fast.
Latency is the amount of time a message takes to traverse a system. In a computer network, it is an expression of how much time it takes for a packet of data to get from one designated point to another. It is sometimes measured as the time required for a packet to be returned to its sender.