Achieving Algorithmic Transparency with Shapley Additive Explanations (H2O Lo...Sri Ambati
Abstract:
Explainability in the age of the EU GDPR is becoming an increasingly pertinent consideration for Machine Learning. At QuantumBlack, we address the traditional Accuracy vs. Interpretability trade-off, by leveraging modern XAI techniques such as LIME and SHAP, to enable individualised explanations without necessary limiting the utility and performance of the otherwise ‘black-box’ models. The talk focuses on Shapley additive explanations (Lundberg et al. 2017) that integrate Shapley values from the Game Theory for consistent and locally accurate explanations; provides illustrative examples and touches upon the wider XAI theory.
Bio:
Dr Torgyn Shaikhina is a Data Scientist at QuantumBlack, STEM Ambassador, and the founder of the Next Generation Programmers outreach initiative. Her background is in decision support systems for Healthcare and Biomedical Engineering with a focus on Machine Learning with limited information.
In this presentation I review various data science techniques and discuss their usefulness to pricing actuaries working in general insurance.
This presentation was originally given at the TIGI webinar in 2020.
https://www.actuaries.org.uk/learn-develop/attend-event/tigi-2020-technical-issues-general-insurance
A HYBRID K-HARMONIC MEANS WITH ABCCLUSTERING ALGORITHM USING AN OPTIMAL K VAL...IJCI JOURNAL
Large quantities of data are emerging every year and an accurate clustering algorithm is needed to derive
information from these data. K-means clustering algorithm is popular and simple, but has many limitations
like its sensitivity to initialization, provides local optimum solutions. K-harmonic means clustering is an
improved variant of K-means which is insensitive to the initialization of centroids, but still in some cases it
ends up with local optimum solutions. Clustering using Artificial Bee Colony (ABC) algorithm always gives
global optimum solutions. In this paper a new hybrid clustering algorithm (KHM-ABC) is presented by
combining both K-harmonic means and ABC algorithm to perform accurate clustering. Experimental
results indicate that the performance of the proposed algorithm is superior to the available algorithms in
terms of the quality of clusters.
Common Problems in Hyperparameter OptimizationSigOpt
Originally given at MLConf NYC 2017.
All large machine learning pipelines have tunable parameters, commonly referred to as hyperparameters. Hyperparameter optimization is the process by which we find the values for these parameters that cause our system to perform the best. SigOpt provides a Bayesian optimization platform that is commonly used for hyperparameter optimization, and I’m going to share some of the common problems we’ve seen when integrating into machine learning pipelines.
In this deck I’m going to show you how SigOpt can help you amplify your trading models by optimally tuning them using our black-box optimization platform.
AWS makes it easy to build, train, tune, and deploy Machine Learning (ML) models. If you're excited to get started with ML on AWS but want a refresher on the ML concepts behind build, train, tune, and deploy, this Dev Chat is for you.
Originally delivered as a Dev Chat at AWS Summit SF by Software Engineer Alexandra Johnson
Achieving Algorithmic Transparency with Shapley Additive Explanations (H2O Lo...Sri Ambati
Abstract:
Explainability in the age of the EU GDPR is becoming an increasingly pertinent consideration for Machine Learning. At QuantumBlack, we address the traditional Accuracy vs. Interpretability trade-off, by leveraging modern XAI techniques such as LIME and SHAP, to enable individualised explanations without necessary limiting the utility and performance of the otherwise ‘black-box’ models. The talk focuses on Shapley additive explanations (Lundberg et al. 2017) that integrate Shapley values from the Game Theory for consistent and locally accurate explanations; provides illustrative examples and touches upon the wider XAI theory.
Bio:
Dr Torgyn Shaikhina is a Data Scientist at QuantumBlack, STEM Ambassador, and the founder of the Next Generation Programmers outreach initiative. Her background is in decision support systems for Healthcare and Biomedical Engineering with a focus on Machine Learning with limited information.
In this presentation I review various data science techniques and discuss their usefulness to pricing actuaries working in general insurance.
This presentation was originally given at the TIGI webinar in 2020.
https://www.actuaries.org.uk/learn-develop/attend-event/tigi-2020-technical-issues-general-insurance
A HYBRID K-HARMONIC MEANS WITH ABCCLUSTERING ALGORITHM USING AN OPTIMAL K VAL...IJCI JOURNAL
Large quantities of data are emerging every year and an accurate clustering algorithm is needed to derive
information from these data. K-means clustering algorithm is popular and simple, but has many limitations
like its sensitivity to initialization, provides local optimum solutions. K-harmonic means clustering is an
improved variant of K-means which is insensitive to the initialization of centroids, but still in some cases it
ends up with local optimum solutions. Clustering using Artificial Bee Colony (ABC) algorithm always gives
global optimum solutions. In this paper a new hybrid clustering algorithm (KHM-ABC) is presented by
combining both K-harmonic means and ABC algorithm to perform accurate clustering. Experimental
results indicate that the performance of the proposed algorithm is superior to the available algorithms in
terms of the quality of clusters.
Common Problems in Hyperparameter OptimizationSigOpt
Originally given at MLConf NYC 2017.
All large machine learning pipelines have tunable parameters, commonly referred to as hyperparameters. Hyperparameter optimization is the process by which we find the values for these parameters that cause our system to perform the best. SigOpt provides a Bayesian optimization platform that is commonly used for hyperparameter optimization, and I’m going to share some of the common problems we’ve seen when integrating into machine learning pipelines.
In this deck I’m going to show you how SigOpt can help you amplify your trading models by optimally tuning them using our black-box optimization platform.
AWS makes it easy to build, train, tune, and deploy Machine Learning (ML) models. If you're excited to get started with ML on AWS but want a refresher on the ML concepts behind build, train, tune, and deploy, this Dev Chat is for you.
Originally delivered as a Dev Chat at AWS Summit SF by Software Engineer Alexandra Johnson
Tuning the Untunable - Insights on Deep Learning OptimizationSigOpt
Patrick Hayes originally gave this talk at ODSC West in 2018. During this talk, Patrick discusses a couple key barriers to deep learning optimization and how SigOpt solves them. First, Patrick discusses the problem of lengthy training cycles and how novel techniques like multitask optimization are designed to use partial information to solve this challenge. Second, Patrick discusses automated cluster management and how solving this problem makes it much easier to manage training cycles for these models.
Advanced Optimization for the Enterprise WebinarSigOpt
Building on the TWIML eBook, TWIMLcon event and TWIML podcast series that explore Machine Learning Platforms in great detail, this webinar examines the machine learning platforms that power enterprise leaders in AI. SigOpt CEO Scott Clark will provide an overview of critical technical capabilities that our customers have prioritized in their ML platforms.
Review these slides to learn about:
- Critical capabilities for data, experiment and model management
- Tradeoffs between building and buying these capabilities
- Lessons from the implementation of these platforms by AI leaders
Why focus on these platforms and the capabilities that power them? Nearly every company is investing in machine learning that differentiates products or generates revenue. These so-called "differentiated models" represent the biggest opportunity for AI to transform the business. Most of these teams find success hiring expert data scientists and machine learning engineers who can build these models. But most of these teams also struggle to create a more sustainable, scalable and reproducible process for model development, and have begun building ML platforms to tackle this challenge.
A review of the paper “Ad Click Prediction: a View from the Trenches”
The paper discusses predicting ad click--through rates (CTR) which is a massive-scale learning problem central to the multi-billion dollar online advertising industry.
Presented by Mazen & Arzam in the Data Intensive Computing class at KTH, Stockholm, Sweden.
Link of the paper: http://research.google.com/pubs/pub41159.html
SigOpt CEO Scott Clark provides insights for modeling at scale in systematic trading. SigOpt works with algorithmic trading firms that collectively represent $300 billion in assets under management (AUM). In this presentation, Scott draws on this experience to provide a few critical insights to how these companies effectively model at scale. Alongside these insights, Scott shares a more specific case study from working with Two Sigma, a leading systematic investment manager.
In this presentation at R user group, I share about the various advance techniques I used for Kaggle competitions. Includes: Interactive visualization via leaflet, geospatial clustering via local Moran's I, feature creation, text categorization via splitTag techniques and ensemble modeling.
Full code can be downloaded here: https://github.com/thiakx/RUGS-Meetup
Train / test data from Kaggle: http://www.kaggle.com/c/see-click-predict-fix/data
Interactive map demo: http://www.thiakx.com/misc/playground/scfMap/scfMap.html
Data Trend Analysis by Assigning Polynomial Function For Given Data SetIJCERT
This paper aims at explaining the method of creating a polynomial equation out of the given data set which can be used as a representation of the data itself and can be used to run aggregation against itself to find the results. This approach uses least-squares technique to construct a model of data and fit to a polynomial. Differential calculus technique is used on this equation to generate the aggregated results that represents the original data set.
SigOpt at O'Reilly - Best Practices for Scaling Modeling PlatformsSigOpt
Companies are increasingly building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. Scott Clark and Matt Greenwood share a case study from a leading algorithmic trading firm to illustrate best practices for building these types of platforms in any industry. Join in to learn how Two Sigma, a leading quantitative investment and technology firm, solved its model optimization problem.
Estimating project development effort using clustered regression approachcsandit
Due to the intangible nature of “software”, accurate and reliable software effort estimation is a
challenge in the software Industry. It is unlikely to expect very accurate estimates of software
development effort because of the inherent uncertainty in software development projects and the
complex and dynamic interaction of factors that impact software development. Heterogeneity
exists in the software engineering datasets because data is made available from diverse sources.
This can be reduced by defining certain relationship between the data values by classifying
them into different clusters. This study focuses on how the combination of clustering and
regression techniques can reduce the potential problems in effectiveness of predictive efficiency
due to heterogeneity of the data. Using a clustered approach creates the subsets of data having
a degree of homogeneity that enhances prediction accuracy. It was also observed in this study
that ridge regression performs better than other regression techniques used in the analysis.
ESTIMATING PROJECT DEVELOPMENT EFFORT USING CLUSTERED REGRESSION APPROACHcscpconf
Due to the intangible nature of “software”, accurate and reliable software effort estimation is a challenge in the software Industry. It is unlikely to expect very accurate estimates of software
development effort because of the inherent uncertainty in software development projects and the complex and dynamic interaction of factors that impact software development. Heterogeneity exists in the software engineering datasets because data is made available from diverse sources.
This can be reduced by defining certain relationship between the data values by classifying them into different clusters. This study focuses on how the combination of clustering and
regression techniques can reduce the potential problems in effectiveness of predictive efficiency due to heterogeneity of the data. Using a clustered approach creates the subsets of data having a degree of homogeneity that enhances prediction accuracy. It was also observed in this study that ridge regression performs better than other regression techniques used in the analysis.
Used a 40GB dataset made available by Avito via Kaggle to demonstrate how to handle big data for machine learning using limited memory. Instead of taking the incremental learning route to train a classifier, we used an intelligent technique to create a representative sample of the dataset.
Since ad clicks are very rare events, naively sampling the data would have lead to significantly biased predictions. This sampling bias was addressed by assigning an importance weight to each data example selected.
The resulting dataset could easily fit into memory and so was then trained using logistic regression.
This talk discusses the intuition behind Bayesian optimization with and without multiple metrics. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.
Tuning for Systematic Trading: Talk 3: Training, Tuning, and Metric StrategySigOpt
This talk explains how you can train and tune efficiently using metric strategy to assign, store, and optimize a variety of metrics, even changing them over time. Tobias Andreassen, who supports a number of our systematic trading customers, explained how he helps customers tune more efficiently with these SigOpt features in real-world scenarios.
Tuning for Systematic Trading: Talk 2: Deep LearningSigOpt
This talk explains how to train deep learning and other expensive models with parallelism and multitask optimization to reduce wall clock time. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
Tuning the Untunable - Insights on Deep Learning OptimizationSigOpt
Patrick Hayes originally gave this talk at ODSC West in 2018. During this talk, Patrick discusses a couple key barriers to deep learning optimization and how SigOpt solves them. First, Patrick discusses the problem of lengthy training cycles and how novel techniques like multitask optimization are designed to use partial information to solve this challenge. Second, Patrick discusses automated cluster management and how solving this problem makes it much easier to manage training cycles for these models.
Advanced Optimization for the Enterprise WebinarSigOpt
Building on the TWIML eBook, TWIMLcon event and TWIML podcast series that explore Machine Learning Platforms in great detail, this webinar examines the machine learning platforms that power enterprise leaders in AI. SigOpt CEO Scott Clark will provide an overview of critical technical capabilities that our customers have prioritized in their ML platforms.
Review these slides to learn about:
- Critical capabilities for data, experiment and model management
- Tradeoffs between building and buying these capabilities
- Lessons from the implementation of these platforms by AI leaders
Why focus on these platforms and the capabilities that power them? Nearly every company is investing in machine learning that differentiates products or generates revenue. These so-called "differentiated models" represent the biggest opportunity for AI to transform the business. Most of these teams find success hiring expert data scientists and machine learning engineers who can build these models. But most of these teams also struggle to create a more sustainable, scalable and reproducible process for model development, and have begun building ML platforms to tackle this challenge.
A review of the paper “Ad Click Prediction: a View from the Trenches”
The paper discusses predicting ad click--through rates (CTR) which is a massive-scale learning problem central to the multi-billion dollar online advertising industry.
Presented by Mazen & Arzam in the Data Intensive Computing class at KTH, Stockholm, Sweden.
Link of the paper: http://research.google.com/pubs/pub41159.html
SigOpt CEO Scott Clark provides insights for modeling at scale in systematic trading. SigOpt works with algorithmic trading firms that collectively represent $300 billion in assets under management (AUM). In this presentation, Scott draws on this experience to provide a few critical insights to how these companies effectively model at scale. Alongside these insights, Scott shares a more specific case study from working with Two Sigma, a leading systematic investment manager.
In this presentation at R user group, I share about the various advance techniques I used for Kaggle competitions. Includes: Interactive visualization via leaflet, geospatial clustering via local Moran's I, feature creation, text categorization via splitTag techniques and ensemble modeling.
Full code can be downloaded here: https://github.com/thiakx/RUGS-Meetup
Train / test data from Kaggle: http://www.kaggle.com/c/see-click-predict-fix/data
Interactive map demo: http://www.thiakx.com/misc/playground/scfMap/scfMap.html
Data Trend Analysis by Assigning Polynomial Function For Given Data SetIJCERT
This paper aims at explaining the method of creating a polynomial equation out of the given data set which can be used as a representation of the data itself and can be used to run aggregation against itself to find the results. This approach uses least-squares technique to construct a model of data and fit to a polynomial. Differential calculus technique is used on this equation to generate the aggregated results that represents the original data set.
SigOpt at O'Reilly - Best Practices for Scaling Modeling PlatformsSigOpt
Companies are increasingly building modeling platforms to empower their researchers to efficiently scale the development and productionalization of their models. Scott Clark and Matt Greenwood share a case study from a leading algorithmic trading firm to illustrate best practices for building these types of platforms in any industry. Join in to learn how Two Sigma, a leading quantitative investment and technology firm, solved its model optimization problem.
Estimating project development effort using clustered regression approachcsandit
Due to the intangible nature of “software”, accurate and reliable software effort estimation is a
challenge in the software Industry. It is unlikely to expect very accurate estimates of software
development effort because of the inherent uncertainty in software development projects and the
complex and dynamic interaction of factors that impact software development. Heterogeneity
exists in the software engineering datasets because data is made available from diverse sources.
This can be reduced by defining certain relationship between the data values by classifying
them into different clusters. This study focuses on how the combination of clustering and
regression techniques can reduce the potential problems in effectiveness of predictive efficiency
due to heterogeneity of the data. Using a clustered approach creates the subsets of data having
a degree of homogeneity that enhances prediction accuracy. It was also observed in this study
that ridge regression performs better than other regression techniques used in the analysis.
ESTIMATING PROJECT DEVELOPMENT EFFORT USING CLUSTERED REGRESSION APPROACHcscpconf
Due to the intangible nature of “software”, accurate and reliable software effort estimation is a challenge in the software Industry. It is unlikely to expect very accurate estimates of software
development effort because of the inherent uncertainty in software development projects and the complex and dynamic interaction of factors that impact software development. Heterogeneity exists in the software engineering datasets because data is made available from diverse sources.
This can be reduced by defining certain relationship between the data values by classifying them into different clusters. This study focuses on how the combination of clustering and
regression techniques can reduce the potential problems in effectiveness of predictive efficiency due to heterogeneity of the data. Using a clustered approach creates the subsets of data having a degree of homogeneity that enhances prediction accuracy. It was also observed in this study that ridge regression performs better than other regression techniques used in the analysis.
Used a 40GB dataset made available by Avito via Kaggle to demonstrate how to handle big data for machine learning using limited memory. Instead of taking the incremental learning route to train a classifier, we used an intelligent technique to create a representative sample of the dataset.
Since ad clicks are very rare events, naively sampling the data would have lead to significantly biased predictions. This sampling bias was addressed by assigning an importance weight to each data example selected.
The resulting dataset could easily fit into memory and so was then trained using logistic regression.
This talk discusses the intuition behind Bayesian optimization with and without multiple metrics. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.
Tuning for Systematic Trading: Talk 3: Training, Tuning, and Metric StrategySigOpt
This talk explains how you can train and tune efficiently using metric strategy to assign, store, and optimize a variety of metrics, even changing them over time. Tobias Andreassen, who supports a number of our systematic trading customers, explained how he helps customers tune more efficiently with these SigOpt features in real-world scenarios.
Tuning for Systematic Trading: Talk 2: Deep LearningSigOpt
This talk explains how to train deep learning and other expensive models with parallelism and multitask optimization to reduce wall clock time. Tobias Andreassen, who supports a number of our systematic trading customers, presented the intuition behind Bayesian optimization for model optimization with a single or multiple (often competing) metrics. Many times it makes sense to analyze a second metric to avoid myopic training runs that overfit on your data, or otherwise don’t represent or impede performance in real-world scenarios.
A Threshold fuzzy entropy based feature selection method applied in various b...IJMER
Large amount of data have been stored and manipulated using various database
technologies. Processing all the attributes for the particular means is the difficult task. To avoid such
difficulties, feature selection process is processed.In this paper,we are collect a eight various benchmark
datasets from UCI repository.Feature selection process is carried out using fuzzy entropy based
relevance measure algorithm and follows three selection strategies like Mean selection strategy,Half
selection strategy and Neural network for threshold selection strategy. After the features are selected,
they are evaluated using Radial Basis Function (RBF) network,Stacking,Bagging,AdaBoostM1 and Antminer
classification methodologies.The test results depicts that Neural network for threshold selection
strategy works well in selecting features and Ant-miner methodology works best in bringing out better
accuracy with selected feature than processing with original dataset.The obtained result of this
experiment shows that clearly the Ant-miner is superiority than other classifiers.Thus, this proposed Antminer
algorithm could be a more suitable method for producing good results with fewer features than
the original datasets.
Proposing an Appropriate Pattern for Car Detection by Using Intelligent Algor...Editor IJCATR
Nowadays, the automotive industry has attracted the attention of consumers, and product quality is considered as an
essential element in current competitive markets. Security and comfort are the main criteria and parameters of selecting a car.
Therefore, standard dataset of CAR involving six features and characteristics and 1728 instances have been used. In this paper, it
has been tried to select a car with the best characteristics by using intelligent algorithms (Random Forest, J48, SVM,
NaiveBayse) and combining these algorithms with aggregated classifiers such as Bagging and AdaBoostMI. In this study, speed
and accuracy of intelligent algorithms in identifying the best car have been taken into account.
Cloudsim a fast clustering-based feature subset selection algorithm for high...ecway
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A fast clustering based feature subset selection algorithm for high-dimension...ecway
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International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology
Feature selection in high-dimensional datasets is
considered to be a complex and time-consuming problem. To
enhance the accuracy of classification and reduce the execution
time, Parallel Evolutionary Algorithms (PEAs) can be used. In
this paper, we make a review for the most recent works which
handle the use of PEAs for feature selection in large datasets.
We have classified the algorithms in these papers into four main
classes (Genetic Algorithms (GA), Particle Swarm Optimization
(PSO), Scattered Search (SS), and Ant Colony Optimization
(ACO)). The accuracy is adopted as a measure to compare the
efficiency of these PEAs. It is noticeable that the Parallel Genetic
Algorithms (PGAs) are the most suitable algorithms for feature
selection in large datasets; since they achieve the highest accuracy.
On the other hand, we found that the Parallel ACO is timeconsuming
and less accurate comparing with other PEA.
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.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
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).
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.
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).
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