This document discusses improving garbage collection performance in Pharo through object lifetime profiling. It presents Illimani, a lifetime profiler developed for Pharo. Illimani was used to profile the lifetimes of objects created when loading a large DataFrame. The profiling revealed that most objects had short lifetimes, suggesting the garbage collector could be tuned. Tuning the garbage collector parameters based on the lifetime profiles improved the performance of loading the DataFrame.
Plenary talk at the international Synchrotron Radiation Instrumentation conference in Taiwan, on work with great colleagues Ben Blaiszik, Ryan Chard, Logan Ward, and others.
Rapidly growing data volumes at light sources demand increasingly automated data collection, distribution, and analysis processes, in order to enable new scientific discoveries while not overwhelming finite human capabilities. I present here three projects that use cloud-hosted data automation and enrichment services, institutional computing resources, and high- performance computing facilities to provide cost-effective, scalable, and reliable implementations of such processes. In the first, Globus cloud-hosted data automation services are used to implement data capture, distribution, and analysis workflows for Advanced Photon Source and Advanced Light Source beamlines, leveraging institutional storage and computing. In the second, such services are combined with cloud-hosted data indexing and institutional storage to create a collaborative data publication, indexing, and discovery service, the Materials Data Facility (MDF), built to support a host of informatics applications in materials science. The third integrates components of the previous two projects with machine learning capabilities provided by the Data and Learning Hub for science (DLHub) to enable on-demand access to machine learning models from light source data capture and analysis workflows, and provides simplified interfaces to train new models on data from sources such as MDF on leadership scale computing resources. I draw conclusions about best practices for building next-generation data automation systems for future light sources.
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar.
In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR.
Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios.
Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects.
Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.
Keynote talk at the International Conference on Supercoming 2009, at IBM Yorktown in New York. This is a major update of a talk first given in New Zealand last January. The abstract follows.
The past decade has seen increasingly ambitious and successful methods for outsourcing computing. Approaches such as utility computing, on-demand computing, grid computing, software as a service, and cloud computing all seek to free computer applications from the limiting confines of a single computer. Software that thus runs "outside the box" can be more powerful (think Google, TeraGrid), dynamic (think Animoto, caBIG), and collaborative (think FaceBook, myExperiment). It can also be cheaper, due to economies of scale in hardware and software. The combination of new functionality and new economics inspires new applications, reduces barriers to entry for application providers, and in general disrupts the computing ecosystem. I discuss the new applications that outside-the-box computing enables, in both business and science, and the hardware and software architectures that make these new applications possible.
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar.
In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR.
Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios.
Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects.
Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.
Micrometrics to forecast performance tsunamisTier1app
Tsunami waves travel at the speed of 500 - 600 miles/hr. Normal waves travel at the speed of 5 - 60 miles/hr. Due to technical limitations, even massive Tsunamis are hard to forecast and detect beforehand. In recent times, hyper sensitive micro-metrics measuring technologies are employed to forecast Tsunamis. Similarly, it’s hard to forecast production performance problems beforehand. In this session you will learn the micro-metrics to be measured in dev/test environments that can forecast production performance problems with a fair level of accuracy.
This is our contributions to the Data Science projects, as developed in our startup. These are part of partner trainings and in-house design and development and testing of the course material and concepts in Data Science and Engineering. It covers Data ingestion, data wrangling, feature engineering, data analysis, data storage, data extraction, querying data, formatting and visualizing data for various dashboards.Data is prepared for accurate ML model predictions and Generative AI apps
Plenary talk at the international Synchrotron Radiation Instrumentation conference in Taiwan, on work with great colleagues Ben Blaiszik, Ryan Chard, Logan Ward, and others.
Rapidly growing data volumes at light sources demand increasingly automated data collection, distribution, and analysis processes, in order to enable new scientific discoveries while not overwhelming finite human capabilities. I present here three projects that use cloud-hosted data automation and enrichment services, institutional computing resources, and high- performance computing facilities to provide cost-effective, scalable, and reliable implementations of such processes. In the first, Globus cloud-hosted data automation services are used to implement data capture, distribution, and analysis workflows for Advanced Photon Source and Advanced Light Source beamlines, leveraging institutional storage and computing. In the second, such services are combined with cloud-hosted data indexing and institutional storage to create a collaborative data publication, indexing, and discovery service, the Materials Data Facility (MDF), built to support a host of informatics applications in materials science. The third integrates components of the previous two projects with machine learning capabilities provided by the Data and Learning Hub for science (DLHub) to enable on-demand access to machine learning models from light source data capture and analysis workflows, and provides simplified interfaces to train new models on data from sources such as MDF on leadership scale computing resources. I draw conclusions about best practices for building next-generation data automation systems for future light sources.
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar.
In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR.
Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios.
Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects.
Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.
Keynote talk at the International Conference on Supercoming 2009, at IBM Yorktown in New York. This is a major update of a talk first given in New Zealand last January. The abstract follows.
The past decade has seen increasingly ambitious and successful methods for outsourcing computing. Approaches such as utility computing, on-demand computing, grid computing, software as a service, and cloud computing all seek to free computer applications from the limiting confines of a single computer. Software that thus runs "outside the box" can be more powerful (think Google, TeraGrid), dynamic (think Animoto, caBIG), and collaborative (think FaceBook, myExperiment). It can also be cheaper, due to economies of scale in hardware and software. The combination of new functionality and new economics inspires new applications, reduces barriers to entry for application providers, and in general disrupts the computing ecosystem. I discuss the new applications that outside-the-box computing enables, in both business and science, and the hardware and software architectures that make these new applications possible.
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar.
In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR.
Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios.
Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects.
Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.
Micrometrics to forecast performance tsunamisTier1app
Tsunami waves travel at the speed of 500 - 600 miles/hr. Normal waves travel at the speed of 5 - 60 miles/hr. Due to technical limitations, even massive Tsunamis are hard to forecast and detect beforehand. In recent times, hyper sensitive micro-metrics measuring technologies are employed to forecast Tsunamis. Similarly, it’s hard to forecast production performance problems beforehand. In this session you will learn the micro-metrics to be measured in dev/test environments that can forecast production performance problems with a fair level of accuracy.
This is our contributions to the Data Science projects, as developed in our startup. These are part of partner trainings and in-house design and development and testing of the course material and concepts in Data Science and Engineering. It covers Data ingestion, data wrangling, feature engineering, data analysis, data storage, data extraction, querying data, formatting and visualizing data for various dashboards.Data is prepared for accurate ML model predictions and Generative AI apps
This is our project work at our startup for Data Science. This is part of our internal training and focused on data management for AI, ML and Generative AI apps
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Presented at the joint US-Japan Workshop on Exascale Computing Collaboration and 6th workshop of US-Japan Joint Institute for Fusion Theory (JIFT) program ( Jan 18th 2022).
This is a final project we worked on the Advances in Data Science course at Northeastern University, Boston.
It deals with object detection and tagging on the fly.
Machine Learning (ML) models are often composed as pipelines of operators, from “classical” ML operators to pre-processing and featurization operators. Current systems deploy pipelines as "black boxes”, where the same implementation of training is run for inference. This solution is convenient but leaves large room to improve performance and resource usage. This talk presents Pretzel, a framework for deployment of ML pipelines that is inspired to Database Systems: Pretzel inspects and optimizes pipelines end-to-end much like queries, and manages resources common to multiple pipelines such as operators' state. Pretzel is joint work with University of Seoul and Microsoft Research and has recently been presented at OSDI ’18. After the overview, this talk also shows experimental results of Pretzel against state-of-art ML solutions and discusses limitations and extensions.
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Authors: G. B. Berriman, J.C. Good, B. Rusholme, T. Robitaille.
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For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-guttmann
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Moses Guttmann, CTO and founder of Allegro, presents the "Optimizing SSD Object Detection for Low-power Devices" tutorial at the May 2019 Embedded Vision Summit.
Deep learning-based computer vision models have gained traction in applications requiring object detection, thanks to their accuracy and flexibility. For deployment on low-power hardware, single-shot detection (SSD) models are attractive due to their speed when operating on inputs with small spatial dimensions.
The key challenge in creating efficient embedded implementations of SSD is not in the feature extraction module, but rather is due to the non-linear bottleneck in the detection stage, which does not lend itself to parallelization. This hinders the ability to lower the processing time per frame, even with custom hardware.
Guttmann describes in detail a data-centric optimization approach to SSD. The approach drastically lowers the number of priors (“anchors”) needed for the detection, and thus linearly decreases time spent on this costly part of the computation. Thus, specialized processors and custom hardware may be better utilized, yielding higher performance and lower latency regardless of the specific hardware used.
This is our project work at our startup for Data Science. This is part of our internal training and focused on data management for AI, ML and Generative AI apps
Analytics Zoo: Building Analytics and AI Pipeline for Apache Spark and BigDL ...Databricks
A long time ago, there was Caffe and Theano, then came Torch and CNTK and Tensorflow, Keras and MXNet and Pytorch and Caffe2….a sea of Deep learning tools but none for Spark developers to dip into. Finally, there was BigDL, a deep learning library for Apache Spark. While BigDL is integrated into Spark and extends its capabilities to address the challenges of Big Data developers, will a library alone be enough to simplify and accelerate the deployment of ML/DL workloads on production clusters? From high level pipeline API support to feature transformers to pre-defined models and reference use cases, a rich repository of easy to use tools are now available with the ‘Analytics Zoo’. We’ll unpack the production challenges and opportunities with ML/DL on Spark and what the Zoo can do
Fast object re-detection and localization in video for spatio-temporal fragme...LinkedTV
Fast object re-detection and localization in video for spatio-temporal fragment creation, Jul. 2013, San Jose, California, USA. Talk provided by Vasileios Mezaris.
Performance Optimization of CGYRO for Multiscale Turbulence SimulationsIgor Sfiligoi
Overview of the recent performance optimization of CGYRO, an Eulerian GyroKinetic Fusion Plasma solver, with emphasize on the Multiscale Turbulence Simulations.
Presented at the joint US-Japan Workshop on Exascale Computing Collaboration and 6th workshop of US-Japan Joint Institute for Fusion Theory (JIFT) program ( Jan 18th 2022).
This is a final project we worked on the Advances in Data Science course at Northeastern University, Boston.
It deals with object detection and tagging on the fly.
Machine Learning (ML) models are often composed as pipelines of operators, from “classical” ML operators to pre-processing and featurization operators. Current systems deploy pipelines as "black boxes”, where the same implementation of training is run for inference. This solution is convenient but leaves large room to improve performance and resource usage. This talk presents Pretzel, a framework for deployment of ML pipelines that is inspired to Database Systems: Pretzel inspects and optimizes pipelines end-to-end much like queries, and manages resources common to multiple pipelines such as operators' state. Pretzel is joint work with University of Seoul and Microsoft Research and has recently been presented at OSDI ’18. After the overview, this talk also shows experimental results of Pretzel against state-of-art ML solutions and discusses limitations and extensions.
The next generation of the Montage image mosaic engineG. Bruce Berriman
Presentation given by Bruce Berriman at the Astronomical Data Analysis Software & Systems XXV (ADASS XXV) Conference, Sydney, Australia, October 29, 2015.
Authors: G. B. Berriman, J.C. Good, B. Rusholme, T. Robitaille.
This presentation was given at the GlobusWorld 2020 Virtual Conference, by Ian Foster, Rachana Ananthakrishnan, and Vas Vasiliadis from the University of Chicago.
Object extraction from satellite imagery using deep learningAly Abdelkareem
Presentation for extract objects from satellite imagery using deep learning techniques. you find a comparison between state-of-art approaches in computer vision.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2019-embedded-vision-summit-guttmann
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Moses Guttmann, CTO and founder of Allegro, presents the "Optimizing SSD Object Detection for Low-power Devices" tutorial at the May 2019 Embedded Vision Summit.
Deep learning-based computer vision models have gained traction in applications requiring object detection, thanks to their accuracy and flexibility. For deployment on low-power hardware, single-shot detection (SSD) models are attractive due to their speed when operating on inputs with small spatial dimensions.
The key challenge in creating efficient embedded implementations of SSD is not in the feature extraction module, but rather is due to the non-linear bottleneck in the detection stage, which does not lend itself to parallelization. This hinders the ability to lower the processing time per frame, even with custom hardware.
Guttmann describes in detail a data-centric optimization approach to SSD. The approach drastically lowers the number of priors (“anchors”) needed for the detection, and thus linearly decreases time spent on this costly part of the computation. Thus, specialized processors and custom hardware may be better utilized, yielding higher performance and lower latency regardless of the specific hardware used.
Field Employee Tracking System| MiTrack App| Best Employee Tracking Solution|...informapgpstrackings
Keep tabs on your field staff effortlessly with Informap Technology Centre LLC. Real-time tracking, task assignment, and smart features for efficient management. Request a live demo today!
For more details, visit us : https://informapuae.com/field-staff-tracking/
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Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
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Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
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Improving Performance Through Object Lifetime Profiling: the DataFrame Case
1. Improving Performance Through
Object Lifetime Pro
fi
ling: the
DataFrame Case
Sebastian JORDAN MONTAÑO, Nahuel PALUMBO, Guillermo POLITO,
Stéphane DUCASSE and Pablo TESONE
Inria, Univ. Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL
August 2023 Evref
fervE
7. An object’s lifetime
Object’s allocation
Object becomes
unreachable
GC collects
the object
Actual lifetime
Lifetime that we capture?
7
8. An object’s approximated lifetime
Object’s allocation
Object becomes
unreachable
GC prepares
the object
Actual lifetime
Lifetime that we capture
Object is
fi
nalized
(we take the measurement)
8
10. Capturing the allocations
Array new: 7
10
Capture the allocation
Register the
fi
nalization
MethodProxies [1]
Ephemerons [2]
[1] github.com/pharo-contributions/MethodProxies
[2] github.com/pharo-project/pheps/blob/main/phep-0003.md
11. An object’s finalization at a time m
11
Ephemeron
#111
Object: Array
#123
Model
allocationTime: n
finalizationTime: m
m
o
u
r
n
finalize
E
p
h
e
m
e
r
o
n
i
s
c
o
n
s
u
m
e
d
1 2
3
key
Finalization Queue
Object #123 is
garbage collected
4
12. An object’s allocation at a time n
12
OrderedCollection class >> new: anInteger
^ self basicNew setCollection:
(self arrayType new: anInteger)
Behavior >> basicNew
<primitive: 70>
Array class >> basicNew: size
lines := OrderedCollection new
...
14. Methodology
14
Application’s Lifetime Profile
P
Application to Profile
3. Chose GC tuned
parameters based on
object lifetimes and
benchmark information
5. Did the performance improved?
GC tuned
parameters
2. Benchmark it with the
default GC parameters
4. Benchmark the application
again with the tuned GC parameters
1. Profile the application
Default GC parameters
benchmark
Tuned GC parameters
benchmark
24. Future work
Measure the precision of our approximate object lifetimes
Pro
fi
ling at VM level to reduce the overhead
Pre-tenuring
24
25. Summary
We developed a lifetime profiler
We profiled the object lifetimes and we validated our solution by
observing how lifetimes relate to performance improvements when
tuning the GC.
25
github.com/jordanmontt/illimani-memory-pro
fi
ler
Sebastian JORDAN MONTAÑO
sebastian.jordan@inria.fr
