Customers are the heart and soul of every organization. As a company grows, it becomes increasingly important to streamline processes while improving the customer experience.
For many companies resolving customer issues and seeking areas for improvement is labor intensive, manual, and unstructured. Statistically, inefficiencies like these increase costs and decrease overall company profitability.
Assess your organizational capability at delivering major projects. Find where you need to improve so you deliver projects to meet expectations. Provides a tool to calculate your capability. www.InsuranceFrameworks.com
Defining a digital transformation maturity modelShekar K. Rao
A proposal for defining a Digital Transformation Maturity Model. This can be used as a starting point for adding on more business levers and will help organisations in identifying their current status on their digital transformation journey.
When it comes to product development, companies have long relied on traditional tools and approaches. By incorporating predictive analytics into the process, organizations can sharpen their forecasts; better predict product performance, failures, and downtime; and generate more value for the business and its customers. Yet doing so requires companies to thoroughly assess their strategic goals, their appetite for investment, and their willingness to experiment.
Beyond the Hype: How to Avoid Common IoT Monetization MistakesGotransverse
Despite the dizzying promises of profit surrounding the Internet of Things (IoT), companies are shying away from pursuing the new revenue opportunities projected. Their reasons, according to a recent KPMG Technology Innovation Survey, include technical complexity, lack of organizational experience, potential disruption and security concerns. These concerns have also led many organizations to make a series of serious mistakes when considering IoT monetization opportunities to pursue.
Watch this recorded webinar to hear directly from Jeff Kaplan of THINKstrategies and Michael Beamer of goTransverse on the following topics:
-Most common IoT mistakes
-Strategies and tactics to successfully monetize IoT initiatives
-Agile monetization platforms and IoT
-Real-world success stories of IoT monetization
Create Success with Analytics: The Business Opportunity of Embedded Analytics...Hannah Flynn
Embedded analytics has evolved from an afterthought to a necessity. But most companies don’t realize that the features they embed and how they develop have a lasting impact on revenue, customer churn, and competitive differentiation.
The state of embedded analytics in 2018 is in flux. Learn from the experiences of 500+ application teams embedding analytics—including which features actually move the needle, how analytics benefits their companies, and what development approach yields the best results.
Customers are the heart and soul of every organization. As a company grows, it becomes increasingly important to streamline processes while improving the customer experience.
For many companies resolving customer issues and seeking areas for improvement is labor intensive, manual, and unstructured. Statistically, inefficiencies like these increase costs and decrease overall company profitability.
Assess your organizational capability at delivering major projects. Find where you need to improve so you deliver projects to meet expectations. Provides a tool to calculate your capability. www.InsuranceFrameworks.com
Defining a digital transformation maturity modelShekar K. Rao
A proposal for defining a Digital Transformation Maturity Model. This can be used as a starting point for adding on more business levers and will help organisations in identifying their current status on their digital transformation journey.
When it comes to product development, companies have long relied on traditional tools and approaches. By incorporating predictive analytics into the process, organizations can sharpen their forecasts; better predict product performance, failures, and downtime; and generate more value for the business and its customers. Yet doing so requires companies to thoroughly assess their strategic goals, their appetite for investment, and their willingness to experiment.
Beyond the Hype: How to Avoid Common IoT Monetization MistakesGotransverse
Despite the dizzying promises of profit surrounding the Internet of Things (IoT), companies are shying away from pursuing the new revenue opportunities projected. Their reasons, according to a recent KPMG Technology Innovation Survey, include technical complexity, lack of organizational experience, potential disruption and security concerns. These concerns have also led many organizations to make a series of serious mistakes when considering IoT monetization opportunities to pursue.
Watch this recorded webinar to hear directly from Jeff Kaplan of THINKstrategies and Michael Beamer of goTransverse on the following topics:
-Most common IoT mistakes
-Strategies and tactics to successfully monetize IoT initiatives
-Agile monetization platforms and IoT
-Real-world success stories of IoT monetization
Create Success with Analytics: The Business Opportunity of Embedded Analytics...Hannah Flynn
Embedded analytics has evolved from an afterthought to a necessity. But most companies don’t realize that the features they embed and how they develop have a lasting impact on revenue, customer churn, and competitive differentiation.
The state of embedded analytics in 2018 is in flux. Learn from the experiences of 500+ application teams embedding analytics—including which features actually move the needle, how analytics benefits their companies, and what development approach yields the best results.
The Business Opportunity of Embedded Analytics: New Findings from 500+ Applic...Aggregage
Embedded analytics has evolved from an afterthought to a necessity. But most companies don’t realize that the features they embed and how they develop have a lasting impact on revenue, customer churn, and competitive differentiation. The state of embedded analytics in 2018 is in flux. Learn from the experiences of 500+ application teams embedding analytics—including which features actually move the needle, how analytics benefits their companies, and what development approach yields the best results.
What if your finance organization had a faster, simpler way to transform operational transactions into meaningful insight? View this slide deck with Workday and KPMG as we explore new technologies and solutions for streamlining the analysis of vast amounts of data in the changing world of finance.
North America Mortgage Banking 2020: Convergent Disruption in the Credit Indu...accenture
To further compound lenders’ challenges to rebuild growth, profitability and efficiency following the recent credit crisis, convergent disruption is leading to a structural change in the industry; multiple disruptive forces are converging, creating an increasingly complex and highly dynamic future environment. Accenture examines the building blocks and roadmap to success in 2020.
An introduction to Account-Based Marketing including an analysis of benefits and criticisms, statistics, implementation suggestions and ideas, Importance of content and Lead-to-Account, challenges, and software considerations.
Contact me on LinkedIn if you have questions: https://www.linkedin.com/in/anantdas
The use of Big Data is becoming a key basis of competition and growth for individual firms. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value from deep and up-to-real-time information.
In the past decade, the HR function has undergone a significant transformation. It has evolved from being a support function to a strategic business driver. Modern day HR’s can leverage plethora of data that to manage Employee Engagement. This Flyer gives an overview about the key features of BRIDGEi2i’s Employee Analytics Management Solution - EmPOWER
CPO ARENA Service Provider Synopsis (Nipendo)Jon Hansen
SEASON 1, EPISODE 4 - Nipendo (Service Provider)
Coming before a global panel of 5 CPOs – Industry Experts all, service providers are invited to present their offering within the framework of the following format:
Show Format
1. In the first 5 minutes, the provider will tell the panel about their company, procurement technology, and their unique value proposition.
2. In the next 7 to 8 minutes, they will demonstrate a unique feature from their technology platform that they believe is a differentiator in the marketplace.
3. In the final 10 minutes, the panel will ask the provider questions based on what they have heard and seen.
4. In the closing 5 minutes, the panel will share their Beefs & Bouquets regarding the provider's solution.
The document you are about to read is a synopsis of the panel’s observations regarding the provider’s solution offering from the standpoint of functionality and potential areas of both benefit and improvement.
Show Replay: https://youtu.be/-W4o7pofpKc
It Takes an Ecosystem: How Technology Companies Deliver Exceptional ExperiencesCognizant
Experience is evolving into a strategy that reaches across technology companies. We offer guidance on the rise of experience and its role in business modernization, with details on how orgnizations can build the ecosystem to support it.
Using Call Driver Analytics to Improve Customer CareRAYA CX
Team Business Area Name: Customer Care
The Client: A Popular International Retail Chain
For this case study, RAYA CX has received Recognition for Excellence in Strategic Partnerships from IAOP. This official recognition represents the quality of support and guidance that RAYA CX provides to its customers.
https://rayacx.info/3MIIaOK
Explainability for Natural Language ProcessingYunyao Li
Final deck for our popular tutorial on "Explainability for Natural Language Processing" at KDD'2021. See links below for downloadable version (with higher resolution) and recording of the live tutorial.
Title: Explainability for Natural Language Processing
Presenter: Marina Danilevsky, Shipi Dhanorkar, Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu
Website: http://xainlp.github.io/
Recording: https://www.youtube.com/watch?v=PvKOSYGclPk&t=2s
Downloadable version with higher resolution: https://drive.google.com/file/d/1_gt_cS9nP9rcZOn4dcmxc2CErxrHW9CU/view?usp=sharing
@article{kdd2021xaitutorial,
title={Explainability for Natural Language Processing},
author= {Marina Danilevsky, Shipi Dhanorkar and Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu},
journal={KDD},
year={2021}
}
Abstract:
This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and interpretability as they relate to building models for text and their applications to knowledge discovery. As black-box models have gained popularity for a broad range of tasks in recent years, both the research and industry communities have begun developing new techniques to render them more transparent and interpretable.Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP/knowledge management researchers, our tutorial has two components: an introduction to explainable AI (XAI) in the NLP domain and a review of the state-of-the-art research; and findings from a qualitative interview study of individuals working on real-world NLP projects as they are applied to various knowledge extraction and discovery at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability inNLP. Then, we will discuss explainability for NLP tasks and reporton a systematic literature review of the state-of-the-art literaturein AI, NLP and HCI conferences. The second component reports on our qualitative interview study, which identifies practical challenges and concerns that arise in real-world development projects that require the modeling and understanding of text data.
CPO ARENA Service Provider Synopsis (Per Angusta)CPOARENA
Season 1, Episode 3 - Per Angusta (Service Provider)
Coming before a global panel of 5 CPOs – Industry Experts all, service providers are invited to present their offering within the framework of the following format:
Show Format
1. In the first 5 minutes, the provider will tell the panel about their company, procurement technology, and their unique value proposition.
2. In the next 7 to 8 minutes, they will demonstrate a unique feature from their technology platform that they believe is a differentiator in the marketplace.
3. In the final 10 minutes, the panel will ask the provider questions based on what they have heard and seen.
4. In the closing 5 minutes, the panel will provide the provider with their preliminary feedback.
The document you are about to read is a synopsis of the panel’s observations regarding the provider’s solution offering from the standpoint of functionality and potential areas of both benefit and improvement.
Show Replay: https://youtu.be/ReXdfJIorDI
How to Get “Rolling” with Monthly ForecastsWorkday, Inc.
The year 2020 showed us that annual plans are apt to change. Moving forward with agility will be key, but implementing a smooth cadence of monthly rolling forecasts might be easier said than done.
View now to see where things often break down from a modeling and data movement perspective, and learn the best ways to get your organization on board for a change.
The Business Opportunity of Embedded Analytics: New Findings from 500+ Applic...Aggregage
Embedded analytics has evolved from an afterthought to a necessity. But most companies don’t realize that the features they embed and how they develop have a lasting impact on revenue, customer churn, and competitive differentiation. The state of embedded analytics in 2018 is in flux. Learn from the experiences of 500+ application teams embedding analytics—including which features actually move the needle, how analytics benefits their companies, and what development approach yields the best results.
What if your finance organization had a faster, simpler way to transform operational transactions into meaningful insight? View this slide deck with Workday and KPMG as we explore new technologies and solutions for streamlining the analysis of vast amounts of data in the changing world of finance.
North America Mortgage Banking 2020: Convergent Disruption in the Credit Indu...accenture
To further compound lenders’ challenges to rebuild growth, profitability and efficiency following the recent credit crisis, convergent disruption is leading to a structural change in the industry; multiple disruptive forces are converging, creating an increasingly complex and highly dynamic future environment. Accenture examines the building blocks and roadmap to success in 2020.
An introduction to Account-Based Marketing including an analysis of benefits and criticisms, statistics, implementation suggestions and ideas, Importance of content and Lead-to-Account, challenges, and software considerations.
Contact me on LinkedIn if you have questions: https://www.linkedin.com/in/anantdas
The use of Big Data is becoming a key basis of competition and growth for individual firms. In most industries, established competitors and new entrants alike will leverage data-driven strategies to innovate, compete, and capture value from deep and up-to-real-time information.
In the past decade, the HR function has undergone a significant transformation. It has evolved from being a support function to a strategic business driver. Modern day HR’s can leverage plethora of data that to manage Employee Engagement. This Flyer gives an overview about the key features of BRIDGEi2i’s Employee Analytics Management Solution - EmPOWER
CPO ARENA Service Provider Synopsis (Nipendo)Jon Hansen
SEASON 1, EPISODE 4 - Nipendo (Service Provider)
Coming before a global panel of 5 CPOs – Industry Experts all, service providers are invited to present their offering within the framework of the following format:
Show Format
1. In the first 5 minutes, the provider will tell the panel about their company, procurement technology, and their unique value proposition.
2. In the next 7 to 8 minutes, they will demonstrate a unique feature from their technology platform that they believe is a differentiator in the marketplace.
3. In the final 10 minutes, the panel will ask the provider questions based on what they have heard and seen.
4. In the closing 5 minutes, the panel will share their Beefs & Bouquets regarding the provider's solution.
The document you are about to read is a synopsis of the panel’s observations regarding the provider’s solution offering from the standpoint of functionality and potential areas of both benefit and improvement.
Show Replay: https://youtu.be/-W4o7pofpKc
It Takes an Ecosystem: How Technology Companies Deliver Exceptional ExperiencesCognizant
Experience is evolving into a strategy that reaches across technology companies. We offer guidance on the rise of experience and its role in business modernization, with details on how orgnizations can build the ecosystem to support it.
Using Call Driver Analytics to Improve Customer CareRAYA CX
Team Business Area Name: Customer Care
The Client: A Popular International Retail Chain
For this case study, RAYA CX has received Recognition for Excellence in Strategic Partnerships from IAOP. This official recognition represents the quality of support and guidance that RAYA CX provides to its customers.
https://rayacx.info/3MIIaOK
Explainability for Natural Language ProcessingYunyao Li
Final deck for our popular tutorial on "Explainability for Natural Language Processing" at KDD'2021. See links below for downloadable version (with higher resolution) and recording of the live tutorial.
Title: Explainability for Natural Language Processing
Presenter: Marina Danilevsky, Shipi Dhanorkar, Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu
Website: http://xainlp.github.io/
Recording: https://www.youtube.com/watch?v=PvKOSYGclPk&t=2s
Downloadable version with higher resolution: https://drive.google.com/file/d/1_gt_cS9nP9rcZOn4dcmxc2CErxrHW9CU/view?usp=sharing
@article{kdd2021xaitutorial,
title={Explainability for Natural Language Processing},
author= {Marina Danilevsky, Shipi Dhanorkar and Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu},
journal={KDD},
year={2021}
}
Abstract:
This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and interpretability as they relate to building models for text and their applications to knowledge discovery. As black-box models have gained popularity for a broad range of tasks in recent years, both the research and industry communities have begun developing new techniques to render them more transparent and interpretable.Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP/knowledge management researchers, our tutorial has two components: an introduction to explainable AI (XAI) in the NLP domain and a review of the state-of-the-art research; and findings from a qualitative interview study of individuals working on real-world NLP projects as they are applied to various knowledge extraction and discovery at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability inNLP. Then, we will discuss explainability for NLP tasks and reporton a systematic literature review of the state-of-the-art literaturein AI, NLP and HCI conferences. The second component reports on our qualitative interview study, which identifies practical challenges and concerns that arise in real-world development projects that require the modeling and understanding of text data.
CPO ARENA Service Provider Synopsis (Per Angusta)CPOARENA
Season 1, Episode 3 - Per Angusta (Service Provider)
Coming before a global panel of 5 CPOs – Industry Experts all, service providers are invited to present their offering within the framework of the following format:
Show Format
1. In the first 5 minutes, the provider will tell the panel about their company, procurement technology, and their unique value proposition.
2. In the next 7 to 8 minutes, they will demonstrate a unique feature from their technology platform that they believe is a differentiator in the marketplace.
3. In the final 10 minutes, the panel will ask the provider questions based on what they have heard and seen.
4. In the closing 5 minutes, the panel will provide the provider with their preliminary feedback.
The document you are about to read is a synopsis of the panel’s observations regarding the provider’s solution offering from the standpoint of functionality and potential areas of both benefit and improvement.
Show Replay: https://youtu.be/ReXdfJIorDI
How to Get “Rolling” with Monthly ForecastsWorkday, Inc.
The year 2020 showed us that annual plans are apt to change. Moving forward with agility will be key, but implementing a smooth cadence of monthly rolling forecasts might be easier said than done.
View now to see where things often break down from a modeling and data movement perspective, and learn the best ways to get your organization on board for a change.
Best Practices for Implementing Self-Service AnalyticsMattSaxton5
Self-service analytics is generally recognized as a valuable asset within corporate strategies, and it’s easy to see why: it provides process experts with the user-friendly tools they need to tackle their day-to-day challenges. It allows problems to be resolved faster and frees up central analytics groups to focus on other pressing issues.
In this ebook, we will share five key learnings from some of our most successful customers in order to help you drive your self-service analytics journey towards success.
Learn more about advanced industrial analytics at www.trendminer.com
Data Science Salon: Adopting Machine Learning to Drive Revenue and Market ShareFormulatedby
The race is on to gain strategic and proprietary insights into changes in customer preferences before your competitors. This workshop will cover how and why machine learning is the tool for marketers to drive revenue and increase market share. The adoption of machine learning does not happen overnight. We will discuss the Five Es of machine learning maturity – Educating, Exploring, Engaging, Executing and Expanding. Hear real-world examples of using machine learning to accelerate revenue, identify new customers and introduce new products based on machine learning capabilities.
Next DSS MIA Event - https://datascience.salon/miami/
Project Deliverable 4 Analytics, Interfaces, and Cloud Technolo.docxwkyra78
Project Deliverable 4: Analytics, Interfaces, and Cloud Technology
By: Justin M. Blazejewski
CIS 499
Professor Dr. Janet Durgin
25 November 2012
Main screen
Overview | Export data | Tools | Realtime | Logout
Current month
Last
Month
Trends
c
Top selling products
Low selling products
Overview
Realtime information
Overview | Export data | Tools | Realtime | Logout
Unique ID
Activity
Result
Overview | Export data | Tools | Realtime | Logout
Reporting tools
Statistical tools
Trends
Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 8.1999999999999993 3.2 1.4 1.2 Sales 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr 8.1999999999999993 3.2 1.4 1.2 Series 1 Category 1 Category 2 Category 3 Category 4 4.3 2.5 3.5 4.5 Series 2 Category 1 Category 2 Category 3 Category 4 2.4 4.4000000000000004 1.8 2.8 Series 3 Category 1 Category 2 Category 3 Category 4 2 2 3 5 Series 1 Category 1 Category 2 Category 3 Category 4 4.3 2.5 3.5 4.5 Series 2 Category 1 Category 2 Category 3 Category 4 2.4 4.4000000000000004 1.8 2.8 Series 3 Category 1 Category 2 Category 3 Category 4 2 2 3 5
Project Deliverable 4: Analytics, Interfaces, and Cloud Technology
By: Justin M. Blazejewski
CIS 499
Professor Dr. Janet Durgin
25 November 2012
Introduction
Business Analytics means the practice of iterative and methodological examination of a business’s data with a special emphasis on statistic making. Business Analytics can further help businesses automate and optimize their business processes. Companies in which data plays a pivotal role, treats its data as a corporate assets and leverages it for gaining competitive advantage. A successful business analytics would typically depend on data quality, highly skillful and experienced professionals who understand the technologies, knows how to work with it and also understands the organizations processes in depth. Apart from this, the organization should have a capable infrastructure to support the operations of business analytics.
Usage of Business Analysis is done for the following purposes:
· Exploration of data so as to find patterns and trends
· Identifying relationships in key data variables for forecasting. For instance next probable purchase by the customer
· Drilling down to the results to find out why a particular incident took place. This approach is done by performing statistical analysis and quantitative analysis with business analytical tools
· Predicting future results by employing predictive modeling and predictive analytics
· Testing previous decisions using A/B and Multivariate testing
· Assisting business in decision making such as figuring out the amount of discount to be given for a new customer
Post identifying of business goal, an analysis methodology needs to be selected and the data is acquired to support the analysis. This data acquisition normally involves extracting data from systems that may be spread throughout different locations an ...
100 day plan - Technology Vision Australian Perspectiveaccenture
Put this 100-day plan into action to gain a deeper understanding of who your core users are and identify opportunities to better serve individual needs.
At Gunaatita, we are a trusted Microsoft BizSpark startup end-to-end Software solutions & Web development Company that provides excellent services in Enterprise mobility solutions, Software product engineering, Content Management System, CRM and Mobile applications using Java & .Net Technologies. We are committed to provide great value to our customers as a trusted offshore partner with our experience and expertise in cutting edge technology and highly process oriented execution.
Our main services include:
. Website Design and Development
. Enterprise Portal Development
. Mobile App Development (Android)
. E-commerce Solutions
. Service API development
Top 6 technologies SME manufacturers can't afford to ignoreIndex InfoTech
Manufacturers today are looking for innovative ways to differentiate themselves in the competitive global marketplace. Understanding customers’ needs in great detail is essential to positioning their company ahead of the competition.
One way to gain this understanding is to implement an enterprise resource planning (ERP) solution. ERP solutions consolidate business operations enhancing your businesses ability to be agile. Not only do these systems deliver on technology that you can boast about over coffee with a colleague, the business advantages are the ability to streamline your organization’s processes to
reduce waste, improve throughput, and ultimately meet your customer’s value stream; improving odds for continued business in the future.
Advanced churn management solution for insurers.Mindtree Ltd.
In today’s competitive marketplace, insurance products have become commoditized. Price comparisons are readily available. Service quality and brand values are being judged based on personal experience and information available through various channels, thus resulting in increased customer churn. Customer retention is getting increasingly important because of growing risk exposures, shrinking profitability and challenges around acquiring new customers.
Visual Analytics combines human intuition and data science to derive knowledge from the data in a very efficient, effective and easy way. Visual Analytics empowers your people to interact with the data and generate new insights.
Fuel for the cognitive age: What's new in IBM predictive analytics IBM SPSS Software
IBM recently launched an updated version of its predictive analytics platform. Explore the latest features, including R, Python and Spark integration and more powerful decision optimization.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.