This chapter discusses principles of scalable performance for parallel systems. It covers performance measures like speedup factors and parallelism profiles. The key principles discussed include degree of parallelism, average parallelism, asymptotic speedup, efficiency, utilization, and quality of parallelism. Performance models like Amdahl's law and isoefficiency concepts are presented. Standard performance benchmarks and characteristics of parallel applications and algorithms are also summarized.
advanced computer architesture-conditions of parallelismPankaj Kumar Jain
This PPT contains Data and Resource Dependencies,Control Dependence,Resource Dependence,Bernstein’s Conditions ,Hardware And Software Parallelism,Types of Software Parallelism
Presentation on Static Network Architecture for multi-programming and multi-processing. Architecture, Ring Architecture, Ring Chordal Architecture, Barrel Shifter Architecture, Fully Connected Architecture.
advanced computer architesture-conditions of parallelismPankaj Kumar Jain
This PPT contains Data and Resource Dependencies,Control Dependence,Resource Dependence,Bernstein’s Conditions ,Hardware And Software Parallelism,Types of Software Parallelism
Presentation on Static Network Architecture for multi-programming and multi-processing. Architecture, Ring Architecture, Ring Chordal Architecture, Barrel Shifter Architecture, Fully Connected Architecture.
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
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
Parallel programming platforms are introduced here. For more information about parallel programming and distributed computing visit,
https://sites.google.com/view/vajira-thambawita/leaning-materials
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.
Remote Procedure Call in Distributed SystemPoojaBele1
Presentation to give description about the remote procedure call in distributed systems
Presentation covers some points on remote procedure call in distributed systems
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
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
Parallel programming platforms are introduced here. For more information about parallel programming and distributed computing visit,
https://sites.google.com/view/vajira-thambawita/leaning-materials
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.
Remote Procedure Call in Distributed SystemPoojaBele1
Presentation to give description about the remote procedure call in distributed systems
Presentation covers some points on remote procedure call in distributed systems
Machine Translation is an emerging field of Computer Science. Researchers have been done to make Machine Translation systems for different language pairs using different practices including rule based machine translation and Statistical Machine Translation (SMT). The goal of the project is to design a Statistical Machine translator for software language localization using Moses decoder. The system is expected to automatically localize (translate) software contents from English into Tamil by using Statistical Machine Translation.
Brown Junior Public School - EQAO School ReportEvanSage
Brown school is situated on Avenue Road just south of St. Clair Avenue. The first school was built in 1910 and was replaced with a new building in 1972. Brown is celebrating its 100th anniversary in 2010. This is a special year for our school celebrating 100 years of excellence. For a century, Brown School has had a tradition of strong academic achievement and close relationships with our families and community. Our staff is a team of dedicated teachers and support staff who bring unique talents to our classrooms, along with many co-curricular activies. Each child is encouraged to strive for academic excellence while also adding balance to the day by pursuing an interest or talent from an extensive number of activities that are offered.
EQAO ensures greater accountability and better quality in Ontario’s publicly funded school system. An arm’s-length agency of the provincial government, EQAO provides parents, teachers and the public with accurate and reliable information about student achievement. EQAO also makes recommendations for improvement that educators, parents, policy-makers and others in the education community can use to improve learning and teaching.
Unit 1: Fundamentals of the Analysis of Algorithmic Efficiency, Units for Measuring Running Time, PROPERTIES OF AN ALGORITHM, Growth of Functions, Algorithm - Analysis, Asymptotic Notations, Recurrence Relation and problems
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
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
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
Essentials of Automations: Optimizing FME Workflows with Parameters
Parallel computing chapter 3
1. Chapter 3: Principles of Scalable
Performance
• Performance measures
• Speedup laws
• Scalability principles
• Scaling up vs. scaling down
EENG-630 Chapter 3 1
2. Performance metrics and
measures
• Parallelism profiles
• Asymptotic speedup factor
• System efficiency, utilization and quality
• Standard performance measures
EENG-630 Chapter 3 2
3. Degree of parallelism
• Reflects the matching of software and
hardware parallelism
• Discrete time function – measure, for each
time period, the # of processors used
• Parallelism profile is a plot of the DOP as a
function of time
• Ideally have unlimited resources
EENG-630 Chapter 3 3
4. Factors affecting parallelism
profiles
• Algorithm structure
• Program optimization
• Resource utilization
• Run-time conditions
• Realistically limited by # of available
processors, memory, and other
nonprocessor resources
EENG-630 Chapter 3 4
5. Average parallelism variables
• n – homogeneous processors
• m – maximum parallelism in a profile
∀ ∆ - computing capacity of a single
processor (execution rate only, no
overhead)
• DOP=i – # processors busy during an
observation period
EENG-630 Chapter 3 5
6. Average parallelism
• Total amount of work performed is
proportional to the area under the profile
curve
t2
W = ∆ ∫ DOP(t )dt
t1
m
W = ∆ ∑ i ⋅ ti
i =1
EENG-630 Chapter 3 6
7. Average parallelism
1 t2
A=
t 2 − t1 ∫t1 DOP(t )dt
m
m
A = ∑ i ⋅ ti / ∑ ti
i =1 i =1
EENG-630 Chapter 3 7
9. Asymptotic speedup
m
m
T (1) = ∑ ti (1) = ∑
m
Wi
T (1) ∑W i
i =1 i =1 ∆ S∞ = = i =1
T (∞ ) m
m
T ( ∞ ) = ∑ ti ( ∞ ) = ∑
m
Wi ∑W /i
i =1
i
i =1 i =1 i∆ = A in the ideal case
(response time)
EENG-630 Chapter 3 9
10. Performance measures
• Consider n processors executing m
programs in various modes
• Want to define the mean performance of
these multimode computers:
– Arithmetic mean performance
– Geometric mean performance
– Harmonic mean performance
EENG-630 Chapter 3 10
11. Arithmetic mean performance
m
Ra = ∑ Ri / m Arithmetic mean execution rate
(assumes equal weighting)
i =1
m
R = ∑ ( f i Ri )
* Weighted arithmetic mean
a execution rate
i =1
-proportional to the sum of the inverses of
execution times
EENG-630 Chapter 3 11
12. Geometric mean performance
m
Rg = ∏ R 1/ m
i
Geometric mean execution rate
i =1
m
R = ∏ Ri
*
g
fi Weighted geometric mean
execution rate
i =1
-does not summarize the real performance since it does
not have the inverse relation with the total time
EENG-630 Chapter 3 12
13. Harmonic mean performance
Ti = 1 / Ri Mean execution time per instruction
For program i
1 m 1 m 1
Ta = ∑ Ti = ∑ Arithmetic mean execution time
m i =1 m i =1 Ri per instruction
EENG-630 Chapter 3 13
14. Harmonic mean performance
m
Rh =1 / Ta = m Harmonic mean execution rate
∑1 / R )
(
i=1
i
1
R =
*
h m
Weighted harmonic mean execution rate
∑( f
i =1
i / Ri )
-corresponds to total # of operations divided by
the total time (closest to the real performance)
EENG-630 Chapter 3 14
15. Harmonic Mean Speedup
• Ties the various modes of a program to the
number of processors used
• Program is in mode i if i processors used
• Sequential execution time T1 = 1/R1 = 1
1
S = T1 / T =
*
∑
n
i =1
f i / Ri
EENG-630 Chapter 3 15
17. Amdahl’s Law
• Assume Ri = i, w = (α, 0, 0, …, 1- α)
• System is either sequential, with probability
α, or fully parallel with prob. 1- α
n
Sn =
1 + (n − 1)α
• Implies S → 1/ α as n → ∞
EENG-630 Chapter 3 17
19. System Efficiency
• O(n) is the total # of unit operations
• T(n) is execution time in unit time steps
• T(n) < O(n) and T(1) = O(1)
S ( N ) = T (1) / T (n)
S (n) T (1)
E ( n) = =
n nT (n)
EENG-630 Chapter 3 19
20. Redundancy and Utilization
• Redundancy signifies the extent of
matching software and hardware parallelism
R (n) = O(n) / O(1)
• Utilization indicates the percentage of
resources kept busy during execution
O ( n)
U ( n) = R ( n) E ( n) =
nT (n20
EENG-630 Chapter 3
)
21. Quality of Parallelism
• Directly proportional to the speedup and
efficiency and inversely related to the
redundancy
• Upper-bounded by the speedup S(n)
3
S ( n) E ( n) T (1)
Q ( n) = = 2
R ( n) nT (n)O(n)
EENG-630 Chapter 3 21
23. Standard Performance Measures
• MIPS and Mflops
– Depends on instruction set and program used
• Dhrystone results
– Measure of integer performance
• Whestone results
– Measure of floating-point performance
• TPS and KLIPS ratings
– Transaction performance and reasoning power
EENG-630 Chapter 3 23
24. Parallel Processing Applications
• Drug design
• High-speed civil transport
• Ocean modeling
• Ozone depletion research
• Air pollution
• Digital anatomy
EENG-630 Chapter 3 24
25. Application Models for Parallel
Computers
• Fixed-load model
– Constant workload
• Fixed-time model
– Demands constant program execution time
• Fixed-memory model
– Limited by the memory bound
EENG-630 Chapter 3 25
26. Algorithm Characteristics
• Deterministic vs. nondeterministic
• Computational granularity
• Parallelism profile
• Communication patterns and
synchronization requirements
• Uniformity of operations
• Memory requirement and data structures
EENG-630 Chapter 3 26
27. Isoefficiency Concept
• Relates workload to machine size n needed
to maintain a fixed efficiency
workload
w( s )
E= overhead
w( s ) + h( s, n)
• The smaller the power of n, the more
scalable the system
EENG-630 Chapter 3 27
28. Isoefficiency Function
• To maintain a constant E, w(s) should grow
in proportion to h(s,n)
E
w( s ) = × h( s, n)
1− E
• C = E/(1-E) is constant for fixed E
f E ( n) = C × h( s, n)
EENG-630 Chapter 3 28
29. Speedup Performance Laws
• Amdahl’s law
– for fixed workload or fixed problem size
• Gustafson’s law
– for scaled problems (problem size increases
with increased machine size)
• Speedup model
– for scaled problems bounded by memory
capacity
EENG-630 Chapter 3 29
30. Amdahl’s Law
• As # of processors increase, the fixed load
is distributed to more processors
• Minimal turnaround time is primary goal
• Speedup factor is upper-bounded by a
sequential bottleneck
• Two cases:
DOP < n
DOP ≥ n
EENG-630 Chapter 3 30
31. Fixed Load Speedup Factor
• Case 1: DOP > n • Case 2: DOP < n
Wi i
ti (i ) = n Wi
i∆ t i ( n ) = t i (∞ ) =
i∆
m
Wi i
T ( n) = ∑ n
i =1 i∆ m
T (1) ∑W i
Sn = = i =i
T ( n) m
Wi i
∑i
i =1
n
EENG-630 Chapter 3 31
32. Gustafson’s Law
• With Amdahl’s Law, the workload cannot
scale to match the available computing
power as n increases
• Gustafson’s Law fixes the time, allowing
the problem size to increase with higher n
• Not saving time, but increasing accuracy
EENG-630 Chapter 3 32
33. Fixed-time Speedup
• As the machine size increases, have
increased workload and new profile
• In general, Wi’ > Wi for 2 ≤ i ≤ m’ and W1’
= W1
• Assume T(1) = T’(n)
EENG-630 Chapter 3 33
34. Gustafson’s Scaled Speedup
'
m
Wi m
i
∑Wi = ∑ i
i =1 i =1
n
+ Q ( n)
m'
∑W i
'
W1 + nWn
S ='
n
i =1
=
m
W1 + Wn
∑W
i =1
i
EENG-630 Chapter 3 34
35. Memory Bounded Speedup
Model
• Idea is to solve largest problem, limited by
memory space
• Results in a scaled workload and higher accuracy
• Each node can handle only a small subproblem for
distributed memory
• Using a large # of nodes collectively increases the
memory capacity proportionally
EENG-630 Chapter 3 35
36. Fixed-Memory Speedup
• Let M be the memory requirement and W
the computational workload: W = g(M)
• g*(nM)=G(n)g(M)=G(n)Wn
m*
∑W i
*
W1 + G (n)Wn
S =
*
n
i =1
=
m*
Wi * i W1 + G (n)Wn / n
∑ i
i =1
n + Q ( n)
EENG-630 Chapter 3 36
37. Relating Speedup Models
• G(n) reflects the increase in workload as
memory increases n times
• G(n) = 1 : Fixed problem size (Amdahl)
• G(n) = n : Workload increases n times when
memory increased n times (Gustafson)
• G(n) > n : workload increases faster than
memory than the memory requirement
EENG-630 Chapter 3 37
38. Scalability Metrics
• Machine size (n) : # of processors
• Clock rate (f) : determines basic m/c cycle
• Problem size (s) : amount of computational
workload. Directly proportional to T(s,1).
• CPU time (T(s,n)) : actual CPU time for
execution
• I/O demand (d) : demand in moving the
program, data, and results for a given run
EENG-630 Chapter 3 38
39. Scalability Metrics
• Memory capacity (m) : max # of memory words
demanded
• Communication overhead (h(s,n)) : amount of
time for interprocessor communication,
synchronization, etc.
• Computer cost (c) : total cost of h/w and s/w
resources required
• Programming overhead (p) : development
overhead associated with an application program
EENG-630 Chapter 3 39
40. Speedup and Efficiency
• The problem size is the independent
parameter
T ( s,1)
S ( s, n) =
T ( s, n) + h( s, n)
S ( s, n)
E ( s, n) =
n
EENG-630 Chapter 3 40
41. Scalable Systems
• Ideally, if E(s,n)=1 for all algorithms and
any s and n, system is scalable
• Practically, consider the scalability of a m/c
S ( s, n) TI ( s, n)
Φ ( s, n) = =
S I ( s, n) T ( s, n)
EENG-630 Chapter 3 41