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Integration of static and dynamic analysis for understanding legacy source code

Michael Moser

OpenACC Highlights: 2019 Year in Review

ACS San Diego - The RDKit: Open-source cheminformatics

Greg Landrum

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OpenACC Monthly Highlights: March 2021

From weather and climate to seismic imaging to aeronautics, OpenACC sessions featured at GTC20 are helping to facilitate discussions, educate attendees and encourage networking and collaboration. Sessions cover a broad range of topics, the “Meet the Experts” session enabled one-on-one deep dives into using OpenACC to solve specific challenges, posters highlight how OpenACC is being applied to current science applications, and the on-demand tutorial delivers hands-on skills building.

Scossu gdi iiif_r+d_report_2019

Stefano Cossu

OpenACC Highlights: GTC Digital April 2020

OpenACC Monthly Highlights February 2019

NVIDIA

OpenACC Monthly Highlights: May 2019

[Presentation by Skylar Lyon at DataWeek 2014, September 17 2014.] I recently faced the task of how to scale out an existing analytics process. The schedule was compressed - it always is in my world. The data was big - 400+ million rows waiting in database. What did I do? I offered my favorite type of solution - quick and dirty. At the outset, I wasn't sure how easy it would be. Nor was I certain of realized performance gains. But the concept seemed sound and the exercise fun. Let's move the compute to the data via Revolution R Enterprise for Teradata. This presentation outlines my approach in leveraging a colleague's R models as I experimented with running R in-database. Would my path lead to significant improvement? Could it be used to productionalize the workflow?

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Info gdal 20150915

GeoMedeelel

Jan2015 bioinfo update_on_ftp_sr_aand_usage

GenomeInABottle

The Beacon v2 Reference Implementation (B2RI) is a free open source Linux-based software created by the Centre for Genomic Regulation (Barcelona, Spain) that allows lighting up a Beacon v2 out-of-the-box.In this training session, a B2RI developer will give an overview on how to use the software to “beaconize” your data (from the user’s perspective). At the end of this training session, participants will be familiarized with the input and output requirements of the B2RI, as well as with the type of queries allowed. This training session was delivered on 17 February 2022 as part of the CINECA GA4GH Beacon series. You can learn about the CINECA project on https://www.cineca-project.eu/

Beacon v2 Reference Implementation: An Overview

CINECAProject

Context: Recent projects such as L4.verified (the verification of the seL4 microkernel) have demonstrated that large-scale formal program verification is now becoming practical. Objective: We address an important but unstudied aspect of proof engineering: proof productivity. Method: We extracted size and effort data from the history of the development of nine projects associated with L4.verified. Results: We find strong linear relationships between effort and proof size for projects and for individuals. We discuss opportunities and limitations with the use of lines of proof as a size measure, and discuss the importance of understanding proof productivity for future research. Conclusions: An understanding of proof productivity will assist in its further industrial application and provide a basis for cost estimation and understanding of rework and tool usage.

167 - Productivity for proof engineering

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Similaire à Know your R usage workflow to handle reproducibility challenges

Presentation from a workshop delivered by Piotr Chaberski during PyData Warsaw Meetup on Feb. 06, 2018. Imagine that you are developing a project using R and your big corporate customer, after weeks of processing requests to establish open-source analytical environment, finally managed to install R on their production machines. Now you realized, that it would be nice to use some Python library in your solution... How would you tell the client to switch to Python for a while?

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WLOG Solutions

Presentation from a workshop delivered by Piotr Chaberski during PyData Warsaw on Oct. 18, 2017. Description Machine Learning is not only about algorithms. Machine learning is about value and this can be achieved only after proper deployment of Machine Learning solutions. I will present best practices regarding managing R based ML projects. Abstract Agenda: I will use our open-source tool R Suite (http://rsuite.io/). During the workshop I will talk about: project structure development cycle repository management deployment During the workshop you will learn about our best practices (e.g. loggers, version control, etc.) we have developed for 12 years of using R. Requirements: Basic R knowledge. Basic ML/DS knowledge. Software installed: R in version 4.3.2 R Suite (latest) R Studio Desktop Operating system: I will be using Windows 10 and this is recommended but Linux should also work.

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Srinivasa Rao M

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Multi-language runtimes providing simultaneously high performance for several programming languages still remain an illusion. Industrial-strength managed language runtimes are built with a focus on one language (e.g., Java or C#). Other languages may compile to the bytecode formats of those managed language runtimes. However, the performance characteristics of the bytecode generation approach are often lagging behind compared to language runtimes specialized for a specific language. The performance of JavaScript is for example still orders of magnitude better on specialized runtimes (e.g., V8 or SpiderMonkey). We present a solution to this problem by providing guest languages with a new way of interfacing with the host runtime. The semantics of the guest language is communicated to the host runtime not via generating bytecodes, but via an interpreter written in the host language. This gives guest languages a simple way to express the semantics of their operations including language-specific mechanisms for collecting profiling feedback. The efficient machine code is derived from the interpreter via automatic partial evaluation. The main components reused from the underlying runtime are the compiler and the garbage collector. They are both agnostic to the executed guest languages. The host compiler derives the optimized machine code for hot parts of the guest language application via partial evaluation of the guest language interpreter. The interpreter definition can guide the host compiler to generate deoptimization points, i.e., exits from the compiled code. This allows guest language operations to use speculations: An operation could for example speculate that the type of an incoming parameter is constant. Furthermore, the guest language interpreter can use global assumptions about the system state that are registered with the compiled code. Finally, part of the interpreter's code can be excluded from the partial evaluation and remain shared across the system. This is useful for avoiding code explosion and appropriate for infrequently executed paths of an operation. These basic mechanisms are provided by the underlying language-agnostic host runtime and allow separation of concerns between guest and host runtime. We implemented Truffle, the guest language runtime framework, on top of the Graal compiler and the HotSpot virtual machine. So far, there are prototypes for C, J, Python, JavaScript, R, Ruby, and Smalltalk running on top of the Truffle framework. The prototypes are still incomplete with respect to language semantics. However, most of them can run non-trivial benchmarks to demonstrate the core promise of the Truffle system: Multiple languages within one runtime system at competitive performance.

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The presentation I delivered at WhyR 2019. Abstract: For many years software engineers have put enormous effort to develop best practices to deliver stable and maintainable software. How R users can benefit from this experience? I will try to answer this question going through several concepts and tools that are natural for software engineers but are often undervalued by R users. I will start with a description of the deployment process because this is the ultimate step that exposes all weaknesses. You will learn about structuring R project, using abstractions to manage model’s features, automating models building process, optimizing the performance of the solution and the challenges of the deployment process itself.

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Presentation delivered at Big Data Tech Warsaw 2019 by me and Maciej Próchniak from TouK. Multiarmed bandits vs simple A/B testing. Architecture of solution – how to connect Flink, Nussknacker and R? Other uses cases – what are other good fits for similar architecture. We observe that many of our customers are actively adapting various marketing automation solutions. While most of them offer some basic A/B testing modules they are often too simple for highly dynamic conditions. Better outcomes can be achieved using e.g. multiarmed bandits algorithms, however, it’s not so straightforward to deploy them in a realtime production environment. In our presentation, we will use a platform based on Apache Flink, Nussknacker – our custom GUI and R Studio + R Suite – everything deployed on Kubernetes.The main goal of our talk is to show how using proposed tools we can create complete flow – from model creation, through deployment and reinforcement learning – that helps to automate marketing communication without the need for custom code development. The talk is partially based on our former deployments of similar solutions, many ideas are new, however.

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Presentation (in polish) I gave at WhyR conference in Warsaw. The abstract: The world of hermetic analytical platforms is slowly becoming history. Today, advanced analytics is being pushed forward by the open-source world supported by the biggest players. In various discussions R's maturity is being questioned if Enterprise point of view is considered. Based on the R deployment in large telecom, I will tell why I claim R can be number one in advanced analytics in any large corporation. I will show what virtues and vices of migrating to R.

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How fast can you modify your data collection to include a new field, make all the necessary changes in data processing and storage, and then use that field in analytics or product features? For many companies, the answer is a few quarters, whereas others do it in a day. This data agility latency has a direct impact on companies' ability to innovate with data. Schema-on-read has been a key strategy to lower that latency - as the community has shifted towards storing data outside relational databases, we no longer need to make series of schema changes through the whole data chain, coordinated between teams to minimise operational risk. Schema-on-read comes with a cost, however. Errors that we used to catch during testing or in early test deployments can now sneak into production undetected and surface as product errors or hard-to-debug data quality problems later than with schema-on-write solutions. In this presentation, we will show how we have rejected the tradeoff between slow schema change rate and quality to achieve the best of both worlds. By using metaprogramming and versioned pipelines that are tested end-to-end, we can achieve fast schema changes with schema-on-write and the protection of static typing. We will describe the tools in our toolbox - Scalameta, Chimney, Bazel, and custom tools. We will also show how we leverage them to take static typing one step further and differentiate between domain types that share representation, e.g. EmailAddress vs ValidatedEmailAddress or kW vs kWh, while maintaining harmony with data technology ecosystems.

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