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
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
DSM system
Shared memory
On chip memory
Bus based multiprocessor
Working through cache
Write through cache
Write once protocol
Ring based multiprocessor
Protocol used
Similarities and differences b\w ring based and bus based
In this presentation, you will learn the fundamentals of Multi Processors and Multi Computers in only a few minutes.
Meanings, features, attributes, applications, and examples of multiprocessors and multi computers.
So, let's get started. If you enjoy this and find the information beneficial, please like and share it with your friends.
A Distributed File System(DFS) is simply a classical model of a file system distributed across multiple machines.The purpose is to promote sharing of dispersed files.
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.
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subscribe the channel
Computer Architecture and Organization
V semester
Anna University
By
Babu M, Assistant Professor
Department of ECE
RMK College of Engineering and Technology
Chennai
Overview of Network Programming, Remote Procedure Calls, Remote Method Invocation, Message Oriented Communication, and web services in distributed systems
There are two primary forms of data exchange between parallel tasks - accessing a shared data space and exchanging messages.
Platforms that provide a shared data space are called shared-address-space machines or multiprocessors.
Platforms that support messaging are also called message passing platforms or multicomputers.
Threads,
system model,
processor allocation,
scheduling in distributed systems
Load balancing and
sharing approach,
fault tolerance,
Real time distributed systems,
Process migration and related issues
A natural extension of the Random Access Machine (RAM) serial architecture is the Parallel Random Access Machine, or PRAM.
PRAMs consist of p processors and a global memory of unbounded size that is uniformly accessible to all processors.
Processors share a common clock but may execute different instructions in each cycle.
MODULE III Parallel Processors and Memory Organization 15 Hours
Parallel Processors: Introduction to parallel processors, Concurrent access to memory and cache
coherency. Introduction to multicore architecture. Memory system design: semiconductor memory
technologies, memory organization. Memory interleaving, concept of hierarchical memory
organization, cache memory, cache size vs. block size, mapping functions, replacement
algorithms, write policies.
Case Study: Instruction sets of some common CPUs - Design of a simple hypothetical CPU- A
sequential Y86-64 design-Sun Ultra SPARC II pipeline structure
DSM system
Shared memory
On chip memory
Bus based multiprocessor
Working through cache
Write through cache
Write once protocol
Ring based multiprocessor
Protocol used
Similarities and differences b\w ring based and bus based
In this presentation, you will learn the fundamentals of Multi Processors and Multi Computers in only a few minutes.
Meanings, features, attributes, applications, and examples of multiprocessors and multi computers.
So, let's get started. If you enjoy this and find the information beneficial, please like and share it with your friends.
A Distributed File System(DFS) is simply a classical model of a file system distributed across multiple machines.The purpose is to promote sharing of dispersed files.
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.
Audio Version available in YouTube Link : https://www.youtube.com/AKSHARAM?sub_confirmation=1
subscribe the channel
Computer Architecture and Organization
V semester
Anna University
By
Babu M, Assistant Professor
Department of ECE
RMK College of Engineering and Technology
Chennai
Overview of Network Programming, Remote Procedure Calls, Remote Method Invocation, Message Oriented Communication, and web services in distributed systems
There are two primary forms of data exchange between parallel tasks - accessing a shared data space and exchanging messages.
Platforms that provide a shared data space are called shared-address-space machines or multiprocessors.
Platforms that support messaging are also called message passing platforms or multicomputers.
Threads,
system model,
processor allocation,
scheduling in distributed systems
Load balancing and
sharing approach,
fault tolerance,
Real time distributed systems,
Process migration and related issues
A natural extension of the Random Access Machine (RAM) serial architecture is the Parallel Random Access Machine, or PRAM.
PRAMs consist of p processors and a global memory of unbounded size that is uniformly accessible to all processors.
Processors share a common clock but may execute different instructions in each cycle.
MODULE III Parallel Processors and Memory Organization 15 Hours
Parallel Processors: Introduction to parallel processors, Concurrent access to memory and cache
coherency. Introduction to multicore architecture. Memory system design: semiconductor memory
technologies, memory organization. Memory interleaving, concept of hierarchical memory
organization, cache memory, cache size vs. block size, mapping functions, replacement
algorithms, write policies.
Case Study: Instruction sets of some common CPUs - Design of a simple hypothetical CPU- A
sequential Y86-64 design-Sun Ultra SPARC II pipeline structure
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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See how to accelerate model training and optimize model performance with active learning
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We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
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Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
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The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
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In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
2. Shared memory architecture
1- Shared memory systems form a major category of multiprocessors.
2- In this category all processors share a global memory.
3- Communication between tasks running on different processors is performed
through writing to and reading from the global memory.
4- All interprocessor coordination and synchronization is also accomplished via
the global memory.
5- A shared memory computer system consists of :
- a set of independent processors.
- a set of memory modules, and an interconnection network as shown in
following Figure 4.1
6- Two main problems need to be addressed when designing a shared memory
system:
• performance degradation due to contention, and
• coherence problems
2
4. Shared memory architecture cont
• Performance degradation might happen when multiple processors are trying to
access the shared memory simultaneously. ?
• Caches : having multiple copies of data, spread throughout the caches might lead
to a coherence problem
- The copies in the caches are coherent if they are all equal to the same value.
- If one of the processors writes over the value of one of the copies, then the copy
becomes inconsistent because it no longer equals the value of the other copies.
4
5. 4.1 CLASSIFICATION OF SHARED
MEMORY SYSTEMS
• The simplest shared memory system consists of one memory module (M)
that can be accessed from two processors (P1 and P2).
• An arbitration unit within the memory module passes requests through to a
memory controller. If the memory module is not busy and a single request
arrives, then the arbitration unit passes that request to the memory
controller and the request is satisfied. The module is placed in the busy
state while a request is being serviced. If a new request arrives while the
memory is busy servicing a previous request, the memory module sends a
wait signal, through the memory controller, to the processor making the new
request. 5
6. 4.1 CLASSIFICATION OF SHARED
MEMORY SYSTEMS cont.
• In response, the requesting processor may hold its request on the line until the
memory becomes free or it may repeat its request some time later. If the
arbitration unit receives two requests, it selects one of them and passes it to the
memory controller. Again, the denied request can be either held to be served
next or it may be repeated some time later.
• Categories of shared memory systems Based on the interconnection network:
Uniform Memory Access (UMA) .
Non-uniform Memory Access (NUMA)
Cache Only Memory Architecture (COMA)
6
7. Uniform Memory Access (UMA)
In the UMA system a shared memory is accessible by all processors through an
interconnection network in the same way a single processor accesses its
memory.
All processors have equal access time to any memory location.
The interconnection network used: a single bus, multiple buses, or a crossbar
switch(diagonal,straight).
Called it SMP (symmetric multiprocessor) systems?
access to shared memory is balanced (Each processor has equal opportunity to
read/write to memory, including equal access speed).
7
8. Nonuniform Memory Access
(NUMA)
• each processor has part of the shared memory attached. The memory has a
single address space. Therefore, any processor could access any memory
location directly using its real address. However, the access time to modules
depends on the distance to the processor.
• the tree and the hierarchical bus networks are architectures used to interconnect
processors.
8
9. Cache-Only Memory Architecture
(COMA)
• Similar to the NUMA, each processor has part of the shared memory in the
COMA. However, in this case the shared memory consists of cache memory.
• requires that data be migrated to the processor requesting it.
• There is no memory hierarchy and the address space is made of all the caches.
There is a cache directory (D) that helps in remote cache access.
9
10. 4.2 BUS-BASED SYMMETRIC
MULTIPROCESSORS
Shared memory systems can be designed using :
• bus-based (simplest network for shared memory systems).
• switch-based interconnection networks (message passing).
The bus/cache architecture alleviates the need for expensive multi-ported
memories and interface circuitry as well as the need to adopt a message-
passing paradigm when developing application software.
the bus may get saturated if multiple processors are trying to access the shared
memory (via the bus) simultaneously. (contention problem) solve uses Caches
High speed caches connected to each processor on one side and the bus on the
other side mean that local copies of instructions and data can be supplied at the
highest possible rate.
If the local processor finds all of its instructions and data in the local cache hit
rate is 100%, otherwise miss rate of a cache, so must be copied from the
global memory, across the bus, into the cache, and then passed on to the local
processor. 10
11. 4.2 BUS-BASED SYMMETRIC
MULTIPROCESSORS cont.
One of the goals of the cache is to maintain a high hit rate, or low miss rate
under high processor loads. A high hit rate means the processors are not using
the bus as much. Hit rates are determined by a number of factors, ranging from
the application programs being run to the manner in which cache hardware is
implemented.
11
12. 4.2 BUS-BASED SYMMETRIC
MULTIPROCESSORS cont.
• We define the variables for hit rate, number of processors, processor speed, bus
speed, and processor duty cycle rates as follows:
N = number of processors;
h = hit rate of each cache, assumed to be the same for all caches;
(1 - h) = miss rate of all caches;
B = bandwidth of the bus, measured in cycles/second;
I = processor duty cycle, assumed to be identical for all processors, in
fetches/cycle; and
V = peak processor speed, in fetches/second
12
13. 4.2 BUS-BASED SYMMETRIC
MULTIPROCESSORS cont.
• The effective bandwidth of the bus is BI fetches/second.
If each processor is running at a speed of V, then misses are being generated
at a rate of V(1 - h). For an N-processor system, misses are simultaneously
being generated at a rate of N(1 - h)V. This leads to saturation of the bus when
N processors simultaneously try to access the bus. That is, 𝑵 𝟏 − 𝒉 𝑽 ≤ 𝑩𝑰.
The maximum number of processors with cache memories that the bus can
support is given by the relation,
𝑁 ≤
𝐵𝐼
1−ℎ 𝑉
13
14. 4.2 BUS-BASED SYMMETRIC
MULTIPROCESSORS cont.
• Example 1
Suppose a shared memory system is constructed from processors that can
execute V = 107 instructions/s and the processor duty cycle I = 1. The caches
are designed to support a hit rate of 97%, and the bus supports a peak
bandwidth of B = 106 cycles/s. Then, (1 - h) = 0.03, and the maximum number
of processors N Is 𝑁 ≤
106∗1
(0.03∗107)
=3.33.
Thus, the system we have in mind can support only three processors!
We might ask what hit rate is needed to support a 30-processor system. In this
case, h = 1- BI/NV = 1 - (106(1))/((30)(107)) = 1 - 1/300, so for the system we
have in mind, h = 0.9967. Increasing h by 2.8%. results in supporting a factor of
ten more processors.
14
15. 4.3 BASIC CACHE COHERENCY
METHODS
• Multiple copies of data, spread throughout the caches, lead to a coherence
problem among the caches. The copies in the caches are coherent if they all
equal the same value. However, if one of the processors writes over the value of
one of the copies, then the copy becomes inconsistent because it no longer
equals the value of the other copies. If data are allowed to become inconsistent
(incoherent), incorrect results will be propagated through the system, leading to
incorrect final results. Cache coherence algorithms are needed to maintain a
level of consistency throughout the parallel system.
Copies data in the
caches
coherence inconsistent
15
16. Cache–Memory Coherence
In a single cache system, coherence between memory and the cache is
maintained using one of two policies:
• write-through:
The memory is updated every time the cache is updated.
• write-back:
The memory is updated only when the block in the cache is being
replaced.
16
18. Cache–Cache Coherence
• In multiprocessing system, when a task running on processor P requests the data in
global memory location X, for example, the contents of X are copied to processor P’s
local cache, where it is passed on to P. Now, suppose processor Q also accesses X.
What happens if Q wants to write a new value over the old value of X?
There are two fundamental cache coherence policies:
write-invalidate
Maintains consistency by reading from local caches until a write occurs.
write-update
Maintains consistency by immediately updating all copies in all caches.
18
20. Shared Memory System
Coherence
• The four combinations to maintain coherence among all caches and
global memory are:
Write-update and write-through
Write-update and write-back
Write-invalidate and write-through
Write-invalidate and write-back
• If we permit a write-update and write-through directly on global memory
location X, the bus would start to get busy and ultimately all processors
would be idle while waiting for writes to complete. In write-update and write-
back, only copies in all caches are updated. On the contrary, if the write is
limited to the copy of X in cache Q, the caches become inconsistent on X.
Setting the dirty bit prevents the spread of inconsistent values of X, but at
some point, the inconsistent copies must be updated.
20