Interpolation is a statistical method by which related known values are used to estimate an unknown price or potential yield of a security. Interpolation is a method of estimating an unknown price or yield of a security. This is achieved by using other related known values that are located in sequence with the unknown value.
Link: https://en.wikipedia.org/wiki/Interpolation
Interpolation is a statistical method by which related known values are used to estimate an unknown price or potential yield of a security. Interpolation is a method of estimating an unknown price or yield of a security. This is achieved by using other related known values that are located in sequence with the unknown value.
Link: https://en.wikipedia.org/wiki/Interpolation
Since its debut in 2010, Apache Spark has become one of the most popular Big Data technologies in the Apache open source ecosystem. In addition to enabling processing of large data sets through its distributed computing architecture, Spark provides out-of-the-box support for machine learning, streaming and graph processing in a single framework. Spark has been supported by companies like Microsoft, Google, Amazon and IBM and in financial services, companies like Blackrock (http://bit.ly/1Q1DVJH ) and Bloomberg (http://bit.ly/29LXbPv ) have started to integrate Apache Spark into their tool chain and the interest is growing. Unlike other big-data technologies which require intensive programming using Java etc., Spark enables data scientists to work with a big-data technology using higher level languages like Python and R making it accessible to conduct experiments and for rapid prototyping.
In this talk, we will introduce Apache Spark and discuss the key features that differentiate Apache Spark from other technologies. We will provide examples on how Apache Spark can help scale analytics and discuss how the machine learning API could be used to solve large-scale machine learning problems using Spark’s distributed computing framework. We will also illustrate enterprise use cases for scaling analytics with Apache Spark.
STIC-D: algorithmic techniques for efficient parallel pagerank computation on...Subhajit Sahu
Authors:
Paritosh Garg
Kishore Kothapalli
Publication:
ICDCN '16: Proceedings of the 17th International Conference on Distributed Computing and Networking. January 2016.
Article No.: 15 Pages 1–10
https://doi.org/10.1145/2833312.2833322
Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey IJECEIAES
In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers.
The aim of the proposed research will be to develop software for implementing a parallel solution for the RSA decryption algorithm. Multithread and distributed computing methods will be used to reach the aimed objective. This effort will include the development of a hybrid OpenMP/MPI program to maximize the use of computational resources and, consequently, decrease the time to decrypt large ciphertexts.
Continuous Intelligence - Intersecting Event-Based Business Logic and MLParis Carbone
Modern data-driven business infrastructure is not as effective as it should be when it comes to critical decision making. Eng-to-End Data pipelines are composed out of fundamentally diverse pieces of tech, each focusing on a specific frontend (e.g., DataFrames, Tensors, Streams) and running in total isolation, thus, being highly unoptimised and complex to integrate with event-based business logic. Our research group has been looking into ways we can use advanced systems theory to compile, optimise and execute distributed functions in unison across the whole spectrum of data-driven programming, leading to a unified way to combine analytics and services all the way down to hardware execution and make continuous intelligence a reality.
Key Takeaways
Introducing the concept of Continuous Intelligence and why we are not there yet.
Pinpointing weaknesses in the current way we structure data-driven pipelines today
Explaining the potential of an Intermediate Representation (IR) and Shared Hardware Execution support to solve the problem.
Presenting our vision on how this new tech can be used to radically change the way we declare and distill knowledge from data in a fast-changing world.
Energy companies deal with huge amounts of data and Apache Spark is an ideal platform to develop machine learning applications for forecasting and pricing. In this talk, we will discuss how Apache Spark’s MLlib library can be used to build scalable analytics for clustering, classification and forecasting primarily for energy applications using electricity and weather datasets.Through a demo, we will illustrate a workflow approach to accomplish an end-to-end pipeline from data pre-processing to deployment for the above use-case using PySpark, Python etc.
Since its debut in 2010, Apache Spark has become one of the most popular Big Data technologies in the Apache open source ecosystem. In addition to enabling processing of large data sets through its distributed computing architecture, Spark provides out-of-the-box support for machine learning, streaming and graph processing in a single framework. Spark has been supported by companies like Microsoft, Google, Amazon and IBM and in financial services, companies like Blackrock (http://bit.ly/1Q1DVJH ) and Bloomberg (http://bit.ly/29LXbPv ) have started to integrate Apache Spark into their tool chain and the interest is growing. Unlike other big-data technologies which require intensive programming using Java etc., Spark enables data scientists to work with a big-data technology using higher level languages like Python and R making it accessible to conduct experiments and for rapid prototyping.
In this talk, we will introduce Apache Spark and discuss the key features that differentiate Apache Spark from other technologies. We will provide examples on how Apache Spark can help scale analytics and discuss how the machine learning API could be used to solve large-scale machine learning problems using Spark’s distributed computing framework. We will also illustrate enterprise use cases for scaling analytics with Apache Spark.
STIC-D: algorithmic techniques for efficient parallel pagerank computation on...Subhajit Sahu
Authors:
Paritosh Garg
Kishore Kothapalli
Publication:
ICDCN '16: Proceedings of the 17th International Conference on Distributed Computing and Networking. January 2016.
Article No.: 15 Pages 1–10
https://doi.org/10.1145/2833312.2833322
Workflow Scheduling Techniques and Algorithms in IaaS Cloud: A Survey IJECEIAES
In the modern era, workflows are adopted as a powerful and attractive paradigm for expressing/solving a variety of applications like scientific, data intensive computing, and big data applications such as MapReduce and Hadoop. These complex applications are described using high-level representations in workflow methods. With the emerging model of cloud computing technology, scheduling in the cloud becomes the important research topic. Consequently, workflow scheduling problem has been studied extensively over the past few years, from homogeneous clusters, grids to the most recent paradigm, cloud computing. The challenges that need to be addressed lies in task-resource mapping, QoS requirements, resource provisioning, performance fluctuation, failure handling, resource scheduling, and data storage. This work focuses on the complete study of the resource provisioning and scheduling algorithms in cloud environment focusing on Infrastructure as a service (IaaS). We provided a comprehensive understanding of existing scheduling techniques and provided an insight into research challenges that will be a possible future direction to the researchers.
The aim of the proposed research will be to develop software for implementing a parallel solution for the RSA decryption algorithm. Multithread and distributed computing methods will be used to reach the aimed objective. This effort will include the development of a hybrid OpenMP/MPI program to maximize the use of computational resources and, consequently, decrease the time to decrypt large ciphertexts.
Continuous Intelligence - Intersecting Event-Based Business Logic and MLParis Carbone
Modern data-driven business infrastructure is not as effective as it should be when it comes to critical decision making. Eng-to-End Data pipelines are composed out of fundamentally diverse pieces of tech, each focusing on a specific frontend (e.g., DataFrames, Tensors, Streams) and running in total isolation, thus, being highly unoptimised and complex to integrate with event-based business logic. Our research group has been looking into ways we can use advanced systems theory to compile, optimise and execute distributed functions in unison across the whole spectrum of data-driven programming, leading to a unified way to combine analytics and services all the way down to hardware execution and make continuous intelligence a reality.
Key Takeaways
Introducing the concept of Continuous Intelligence and why we are not there yet.
Pinpointing weaknesses in the current way we structure data-driven pipelines today
Explaining the potential of an Intermediate Representation (IR) and Shared Hardware Execution support to solve the problem.
Presenting our vision on how this new tech can be used to radically change the way we declare and distill knowledge from data in a fast-changing world.
Energy companies deal with huge amounts of data and Apache Spark is an ideal platform to develop machine learning applications for forecasting and pricing. In this talk, we will discuss how Apache Spark’s MLlib library can be used to build scalable analytics for clustering, classification and forecasting primarily for energy applications using electricity and weather datasets.Through a demo, we will illustrate a workflow approach to accomplish an end-to-end pipeline from data pre-processing to deployment for the above use-case using PySpark, Python etc.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
3. Abstract
This paper presets results of computational
finance experiments using map-reduce in Scala.
They observe superlinear speedup, superefficiency, and evidence for a high degree of
compute and I/O overlap in the median
runtimes using “naïve,” memory-bound, finegrain, and course-grain parallel algorithms on
three different hardware platforms.
4. Computational finance is a multidisciplinary field
at the crossroads of mathematical finance and
computer science. The emphasis is on
development and utilization of numerically
intensive methods for pricing, risk analysis,
forecasting, automated trading, and other
applications.
6. Map-reduce is a framework generally to speedup data analysis using distributed computing.
While map-reduce has been applied to different
problem domains, many of a data-intensive
nature, almost no attention has been given to
opportunities for computational finance as a
mixture of floating point and data-intensive
operations.
7.
8. Scala is a modern, high-level Java Virtual Machine (JVM)
language that blends object-oriented and functional
programming styles with actors, a shared nothing model
of concurrent computation inspired by physics theories.
Proponents have argued that Scala language features are
suited to solving large-scale computing tasks on
inexpensive, commodity multicore and multiprocessor
platforms in an expressive manner that avoids the
concurrency hazards and runtime inefficiencies of shared,
mutable state programs. Indeed, the function-oriented
style of Scala would seem to lend itself precisely to
coding mathematical expressions which characterize
quantitative operations.
10. Related work
• The literature shows enduring interest in
speeding up computational finance
algorithms.
• The literature furthermore indicates mapreduce is a widely accepted approach to
speeding up computation for various problem
classes.
11. Method
•
•
•
•
•
•
•
•
Bond pricing theory
Bond generation algorithm
IO design
Pricing algorithms
Serial algorithms
Parallel naïve algorithm
Parallel coarse-grain algorithm
Parallel fine-grain algorithm
21. • The naïve algorithm appears to be the best performing
overall end-to-end, achieving super-linearity and
superefficiency for levels of u, depending on the
processor type. For instance, the more modern
processors, the W3540 and i5, realize super-linearity
and superefficiency for u as small as 64.
• I/O is broadly sub-linear which, by itself, is not
surprising. However, I/O does not appear to be a
processing bottleneck since the difference between
compute and memory-bound compute plus
memorybound I/O over the range of u appears to be
insignificant.
22. Conclusion
• They would like to explore changes to H-S to
support multiprocessor parallelism.
• there are open questions on how to “shard”
or parallelize the data.
• we had briefly mentioned Scala’s parallel
collections.
