Parallel computing uses multiple processors or computers simultaneously to solve problems faster than a single processor. While n processors could theoretically provide an n times speedup, in reality various factors limit speedup. Parallel computing aims to solve "grand challenge" problems like modeling large DNA structures or global weather forecasting that would take too long on today's computers. It works by dividing a large problem into sub-problems that can be solved concurrently. The maximum theoretical speedup is limited by the fraction of a problem that must be solved sequentially. In practice, speedup depends on how effectively a problem can be divided into parallelizable parts.
Elementary Parallel Algorithm - Sum of n numbers on Hypercube, Shuffle Exchange and Mesh SIMD computers, UMA multiprocessors, Broadcasting and pre-fix sum on multicomputer.
Algorithm and its Properties
Computational Complexity
TIME COMPLEXITY
SPACE COMPLEXITY
Complexity Analysis and Asymptotic notations.
Big-oh-notation (O)
Omega-notation (Ω)
Theta-notation (Θ)
The Best, Average, and Worst Case Analyses.
COMPLEXITY Analyses EXAMPLES.
Comparing GROWTH RATES
Elementary Parallel Algorithm - Sum of n numbers on Hypercube, Shuffle Exchange and Mesh SIMD computers, UMA multiprocessors, Broadcasting and pre-fix sum on multicomputer.
Algorithm and its Properties
Computational Complexity
TIME COMPLEXITY
SPACE COMPLEXITY
Complexity Analysis and Asymptotic notations.
Big-oh-notation (O)
Omega-notation (Ω)
Theta-notation (Θ)
The Best, Average, and Worst Case Analyses.
COMPLEXITY Analyses EXAMPLES.
Comparing GROWTH RATES
STATISTICAL APPROACH TO DETERMINE MOST EFFICIENT VALUE FOR TIME QUANTUM IN RO...ijcsit
Scheduling various processes is one of the most fundamental functions of the operating system. In that
context one of the most common scheduling algorithms used in most operating systems is the Round Robin
method in which, the ready processes waiting in the ready queue, take control of the processor for a short
period of time known as the time quantum (or time slice) circularly. Here we discuss the use of statistics
and develop a mathematical model to determine the most efficient value for time quantum. This is strictly
theoretical as we do not know the values of times for the various processes beforehand. However the
proposed approach is compared with the recent developed algorithms to this regard to determine the
efficiency of the proposed algorithm.
Keep Calm and React with Foresight: Strategies for Low-Latency and Energy-Eff...Tiziano De Matteis
This talk has been given at PPoPP 2016 (Barcelona)
The paper addresses the problem of designing control strategies for elastic stream processing applications. Elasticity allows applications to rapidly change their configuration (e.g. the number of used resources) on-the-fly, in response to fluctuations of their workload. In this work we face this problem by adopting the Model Predictive Control technique, a control-theoretic method aimed at finding the optimal application configuration along a limited prediction horizon by solving an online optimization problem. Our control strategies are designed to address latency constraints, by using Queueing Theory models, and energy consumption by changing the number of used cores and the CPU frequency through the Dynamic Voltage and Frequency Scaling (DVFS) function of modern multi-core CPUs. The proactive capabilities, in addition to the latency- and energy-awareness, represent the novel features of our approach. Experiments performed using a high-frequency trading application show the effectiveness compared with state-of-the-art techniques.
A full version of the slides (with transitions) is available at: https://docs.google.com/presentation/d/1VZ3y3RQDLFi_xA7Rl0Vj1iqBdoerxCMG4y53uMz9Ziw/edit?usp=sharing
STATISTICAL APPROACH TO DETERMINE MOST EFFICIENT VALUE FOR TIME QUANTUM IN RO...ijcsit
Scheduling various processes is one of the most fundamental functions of the operating system. In that
context one of the most common scheduling algorithms used in most operating systems is the Round Robin
method in which, the ready processes waiting in the ready queue, take control of the processor for a short
period of time known as the time quantum (or time slice) circularly. Here we discuss the use of statistics
and develop a mathematical model to determine the most efficient value for time quantum. This is strictly
theoretical as we do not know the values of times for the various processes beforehand. However the
proposed approach is compared with the recent developed algorithms to this regard to determine the
efficiency of the proposed algorithm.
Keep Calm and React with Foresight: Strategies for Low-Latency and Energy-Eff...Tiziano De Matteis
This talk has been given at PPoPP 2016 (Barcelona)
The paper addresses the problem of designing control strategies for elastic stream processing applications. Elasticity allows applications to rapidly change their configuration (e.g. the number of used resources) on-the-fly, in response to fluctuations of their workload. In this work we face this problem by adopting the Model Predictive Control technique, a control-theoretic method aimed at finding the optimal application configuration along a limited prediction horizon by solving an online optimization problem. Our control strategies are designed to address latency constraints, by using Queueing Theory models, and energy consumption by changing the number of used cores and the CPU frequency through the Dynamic Voltage and Frequency Scaling (DVFS) function of modern multi-core CPUs. The proactive capabilities, in addition to the latency- and energy-awareness, represent the novel features of our approach. Experiments performed using a high-frequency trading application show the effectiveness compared with state-of-the-art techniques.
A full version of the slides (with transitions) is available at: https://docs.google.com/presentation/d/1VZ3y3RQDLFi_xA7Rl0Vj1iqBdoerxCMG4y53uMz9Ziw/edit?usp=sharing
This talk is given at Vizianagaram where many Engineering college faculty were attended. I have introduced developments in multi-core computers along with their architectural developments. Also, I have explained about high performance computing, where these are used. I have introduced the concept of pipelining, Amdahl's law, issues related to pipelining, MIPS architecture.
High Performance & High Throughput Computing - EUDAT Summer School (Giuseppe ...EUDAT
Giuseppe will present the differences between high-performance and high-throughput applications. High-throughput computing (HTC) refers to computations where individual tasks do not need to interact while running. It differs from High-performance (HPC) where frequent and rapid exchanges of intermediate results is required to perform the computations. HPC codes are based on tightly coupled MPI, OpenMP, GPGPU, and hybrid programs and require low latency interconnected nodes. HTC makes use of unreliable components distributing the work out to every node and collecting results at the end of all parallel tasks.
Visit: https://www.eudat.eu/eudat-summer-school
Temporal workload analysis and its application to power aware schedulingijesajournal
Power
-
aware scheduling reduces CPU energy consumption in hard real
-
time systems through dynamic
voltage scaling(DVS). The basic idea of power
-
aware scheduling
is to find slacks available to tasks and
reduce CPU‟s frequency or lower its voltage using the found slacks. In this paper, we introduce temporal
workload of a system which specifies how much busy its CPU is to complete the tasks at current time.
Analyzin
g temporal workload provides a sufficient condition of schedulability of preemptive early
-
deadline
first scheduling and an effective method to identify and distribute slacks generated by early completed
tasks. The simulation results show that proposed algo
rithm reduces the energy consumption by 10
-
70%
over the existing algorithm and its algorithm complexity is O(n). So, practical on
-
line scheduler could be
devised using the proposed algorithm.
Temporal workload analysis and its application to power aware schedulingijesajournal
Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic voltage scaling(DVS). The basic idea of power-aware scheduling is to find slacks available to tasks and reduce CPU‟s frequency or lower its voltage using the found slacks. In this paper, we introduce temporal workload of a system which specifies how much busy its CPU is to complete the tasks at current time. Analyzing temporal workload provides a sufficient condition of schedulability of preemptive early-deadline first scheduling and an effective method to identify and distribute slacks generated by early completed tasks. The simulation results show that proposed algorithm reduces the energy consumption by 10-70% over the existing algorithm and its algorithm complexity is O(n). So, practical on-line scheduler could be devised using the proposed algorithm.
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
2. 1a.2
Parallel Computing
• Using more than one computer, or a computer with
more than one processor, to solve a problem.
Motives
• Usually faster computation.
• Very simple idea
– n computers operating simultaneously can achieve
the result faster
– it will not be n times faster for various reasons
• Other motives include: fault tolerance, larger amount
of memory available, ...
3. 1a.3
Demand for Computational Speed
• Continual demand for greater computational
speed from a computer system than is
currently possible
• Areas requiring great computational speed
include:
– Numerical modeling
– Simulation of scientific and engineering problems.
• Computations need to be completed within a
“reasonable” time period.
4. 1a.4
“Grand Challenge” Problems
Ones that cannot be solved in a reasonable
amount of time with today’s computers.
Obviously, an execution time of 10 years is
always unreasonable.
Grand Challenge Problem Examples
• Modeling large DNA structures
• Global weather forecasting
• Modeling motion of astronomical bodies.
5. 1a.5
Weather Forecasting
• Atmosphere modeled by dividing it into 3-
dimensional cells.
• Calculations of each cell repeated many
times to model passage of time.
Temperature,
pressure,
humidity, etc.
6. 1a.6
Global Weather Forecasting Example
• Suppose whole global atmosphere divided into cells of size 1
mile × 1 mile × 1 mile to a height of 10 miles (10 cells high) -
about 5 × 108
cells.
• Suppose each calculation requires 200 floating point
operations. In one time step, 1011
floating point operations
necessary.
• To forecast weather over 7 days using 1-minute intervals, a
computer operating at 1Gflops (109
floating point operations/s)
takes 106
seconds or over 10 days.
• To perform calculation in 5 minutes requires computer
operating at 3.4 Tflops (3.4 × 1012
floating point operations/sec)
• Needs to be 34,000 faster.
7. 1a.7
Modeling Motion of Astronomical Bodies
Each body attracted to each other body by
gravitational forces. Movement of each body
predicted by calculating total force on each body.
8. 1a.8
Modeling Motion of Astronomical Bodies
• With N bodies, N - 1 forces to calculate for each
body, or approx. N2
calculations, i.e. O(N2
) *
• After determining new positions of bodies,
calculations repeated, i.e. N2
x T calculations
where T is the number of time steps.
* There is an O(N log2 N) algorithm, which we will cover in the
course
9. 1a.9
• A galaxy might have, say, 1011
stars.
• Even if each calculation done in 1 ms (extremely
optimistic figure), it takes:
• 109
years for one iteration using N2
algorithm
or
• Almost a year for one iteration using the N log2 N
algorithm assuming the calculations take the same
time (which may not be true).
12. 1a.12
Gill writes in 1958:
“... There is therefore nothing new in the idea of parallel
programming, but its application to computers. The
author cannot believe that there will be any insuperable
difficulty in extending it to computers. It is not to be
expected that the necessary programming techniques will
be worked out overnight. Much experimenting remains to
be done. After all, the techniques that are commonly
used in programming today were only won at the cost of
considerable toil several years ago. In fact the advent of
parallel programming may do something to revive the
pioneering spirit in programming which seems at the
present to be degenerating into a rather dull and routine
occupation ...”
Gill, S. (1958), “Parallel Programming,” The Computer Journal, vol. 1, April, pp. 2-10.
14. 1a.14
Speedup Factor
where ts is execution time on a single processor and tp is
execution time on a multiprocessor.
S(p) gives increase in speed by using multiprocessor.
Typically use best sequential algorithm with single
processor system. Underlying algorithm for parallel
implementation might be (and is usually) different.
S(p) =
Execution time using one processor (best sequential algorithm)
Execution time using a multiprocessor with p processors
ts
tp
=
15. 1a.15
Speedup factor can also be cast in terms
of computational steps:
Can also extend time complexity to
parallel computations.
S(p) =
Number of computational steps using one processor
Number of parallel computational steps with p processors
16. 1a.16
Maximum Speedup
Maximum speedup usually p with p processors
(linear speedup).
Possible to get superlinear speedup (greater
than p) but usually a specific reason such as:
• Extra memory in multiprocessor system
• Nondeterministic algorithm
18. 1a.18
Speedup factor is given by:
This equation is known as Amdahl’s law
S(p) =
ts p=
fts + (1− f )ts /p 1 + (p − 1)f
19. 1a.19
Speedup against number of processors
4
8
12
16
20
4 8 12 16 20
f = 20%
f = 10%
f = 5%
f = 0%
Number of processors , p
20. 1a.20
Even with infinite number of processors, maximum
speedup limited to 1/f.
Example
With only 5% of computation being serial, maximum
speedup is 20, irrespective of number of processors.
This is a very discouraging result.
Amdahl used this argument to support the design of
ultra-high speed single processor systems in the
1960s.
21. Gustafson’s law
Later, Gustafson (1988) described how the
conclusion of Amdahl’s law might be overcome by
considering the effect of increasing the problem
size.
He argued that when a problem is ported onto a
multiprocessor system, larger problem sizes can
be considered, that is, the same problem but with a
larger number of data values.
1a.21
22. Gustafson’s law
Starting point for Gustafson’s law is the computation
on the multiprocessor rather than on the single
computer.
In Gustafson’s analysis, parallel execution time kept
constant, which we assume to be some acceptable
time for waiting for the solution.
1a.22
23. Gustafson’s law
Parallel computation composed of fraction computed sequentially
say f ’ and fraction that contains parallel parts,1 – f ’.
Gustafson’s so-called scaled speedup fraction given by:
f ’ is fraction of computation on multiprocessor that cannot be
parallelized.
f ’ is different to f previously, which is fraction of computation on a
single computer that cannot be parallelized.
Conclusion drawn from Gustafson’s law is almost linear increase
in speedup with increasing number of processors, but the
fractional part f ‘ needs to remain small. 1a.23
S’(p) =
f ’tp + (1 – f ’)ptp
tp
= p + (1 – p)f ’
24. Gustafson’s law
For example if is 5%, the scaled speedup computes
to 19.05 with 20 processors whereas with Amdahl’s
law with f = 5% the speedup computes to 10.26.
Gustafson quotes results obtained in practice of very
high speedup close to linear on a 1024-processor
hypercube.
1a.24
25. 1a.25
Superlinear Speedup example
- Searching
(a) Searching each sub-space sequentially
ts
ts/p
Start Time
∆t
Solution found
xts/p
Sub-space
search
x indeterminate
28. 1a.28
Worst case for sequential search when solution
found in last sub-space search. Then parallel
version offers greatest benefit, i.e.
S(p)
p 1–
p
t
s
t∆+×
t∆
∞→=
as ∆t tends to zero
29. 1a.29
Least advantage for parallel version when
solution found in first sub-space search of
the sequential search, i.e.
Actual speed-up depends upon which
subspace holds solution but could be
extremely large.
S(p) = t∆
t∆
= 1
30. 1a.30
• Next question to answer is how does
one construct a computer system with
multiple processors to achieve the
speed-up?