TreeHouse, nourish your community
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- Silvia Brembati, Product Designer - Benedetta Bolis, Engineering Physics Student Due to the recent COVID-19 outbreak, everybody had to quickly rearrange their lifestyle and learn how to get through isolation. Keeping in touch has never been more compelling and challenging at the same time. A recent survey conducted in Italy, states that 80% of the population felt like they needed psychological support to get through quarantine. We believe that if people had a way to feel surrounded by their friends and had been able to share activities, this number would be significantly lower. This is where our new app TreeHouse comes in handy as it guides the user in contributing to the life of the community: a virtual tree will come to life and thrive thanks to both real-life and online interactions. Sharing content, chatting with friends, or drinking a cup of tea together will make a leaf or a branch grow, but if the user is missing for too long, the tree will suffer from their absence, in complete symbiosis. Nevertheless, checking how the tree develops helps the members feel the actual presence of the community, and makes them able to support each other, letting the tree flourish again.
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- Edoardo Ramalli, M.Sc. student in Computer Science and Engineering
Drug Repurposing is the investigation of existing drugs on the pharmaceutical market for new therapeutic purposes; drug repurposing reduces the time and cost of clinical trial steps, saving years, and billions of dollars in R&D. Identifying new diseases on which a drug can be effective is a complex problem: our approach leverages knowledge graphs (KG), networks composed of many types of entities and relations, on which embedding and graph completion techniques can be applied to infer insights and analyses. Our KG is built from well-known databases such as DrugBank, UniProt, and CTD and contains over one million relationships between more than 70K biological and pharmaceutical entities like diseases, genes, proteins and drugs. In this work, we research the applicability of knowledge graph completion techniques, such as link prediction (and triple classification) using a various number of different embedding models from different families: matrix factorization, geometric and Deep learning. Using these models is possible to infer new drug-disease relationships on our KG, and identify novel drug repurposing candidates. Preliminary experimental results are encouraging and show how state-of-the-art machine learning models, combined with the ever-growing amount of biological data freely available to the research community, could significantly improve the field of drug repurposing.Embedding based knowledge graph link prediction for drug repurposing
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Plaster is a multi-layered infrastructure (based on C++) aimed at supporting the development of multi-FPGA systems and the management of large data flows between the nodes. In particular, the goal of the project is to provide the end-user with a set of tools (by the means of a Python library and a C++ service) to easily assign bitstreams to nodes and route data between them, in the context of a PYNQ-based cluster suitable for distributed acceleration of computation-intensive tasks. Using this platform, an abandoned objects detection tool is implemented, designed as a Multi-FPGA distributed system exploiting an hardware accelerated version of the YOLO neural network for image detection.PLASTER - PYNQ-based abandoned object detection using a map-reduce approach o...
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- Luca Stornaiuolo, PhD student in Computer Science @Politecnico di Milano EMPhASIS - An EMbedded Public Attention Stress Identification System
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Il secondo semestre dell’A.A. 2019/20 non lo scorderemo tanto facilmente: nel momento di pausa tra la fine degli appelli d’esame e la ripartenza delle lezioni il mondo ci è cambiato sotto gli occhi. All’improvviso abbiamo dovuto reinventarci un modo per erogare la didattica del secondo semestre, per consentire ai nostri ragazzi di laurearsi, per far loro sostenere gli esami. In poco meno di due settimane il nostro Ateneo è riuscito a convertire da didattica in presenza a online circa 1400 insegnamenti: una massiccia prova di resilienza e adattamento al cambiamento. Non è stato facile, ha presentato molti problemi, ma è anche una straordinaria opportunità che adesso dobbiamo imparare a cogliere, per non perdere quanto di buono (ed è parecchio) è stato fatto in questi mesi complicati. Mesi in cui, per citare lo storico Yuval Harari decisioni che in tempi normali richiederebbero anni di attenta valutazione sono state approvate nel giro di poche ore.NECSTTechTalk: La didattica del Politecnico di Milano (e non solo!) ai tempi ...
NECSTTechTalk: La didattica del Politecnico di Milano (e non solo!) ai tempi ...NECST Lab @ Politecnico di Milano
Abstract inglese:
In recent years Graphic Processing Units have seen widespread adoption in many scientific fields, from Machine Learning (ML) to Genomics. Their use makes it possible to achieve significant speedups and improvements in power efficiency over computationally intensive algorithms compared to General Purpose Central Processing Units. However, algorithms require specific knowledge of the GPU architecture and expertise to achieve significant results. In this work, we describe a methodology for automatic GPU kernel optimization. Our methodology exploits the Berkeley Roofline Model to perform a performance analysis of the algorithm considered and aims to increase the accessibility of GPU programming automatizing the optimization process of the kernel. We provide an in-depth analysis of this methodology, an overview on the state of the art, and a description of a tool we developed that automatically applies our methodology to obtain a highly optimized GPU version of two of the most popular algorithms used in computational biology, the X-drop and Smith-Waterman algorithms. The Smith-Waterman algorithm is one of the most used algorithms in genomics pipelines. The algorithm finds the optimal local alignment between two genomic sequences, at the cost of being particularly compute-intensive. The popular X-drop algorithm reduces the time required by the alignment by searching only for high-quality alignments. The algorithms accelerated using our methodology achieve more than 6x and 3x speed-up, for the X-drop and Smith-Waterman algorithms respectively, with respect to the state of the art implementation of these algorithms.A Methodology for Automatic GPU Kernel Optimization - NECSTTechTalk 4/06/2020
A Methodology for Automatic GPU Kernel Optimization - NECSTTechTalk 4/06/2020NECST Lab @ Politecnico di Milano
Outstanding advancements in imaging technology have made cryogenic electron microscopy a powerful technique for the nanocharacterization of biological macromolecular complexes, reaching atomic levels of resolution and being applicable to a wider set of samples than the other competing technologies. The real breakthrough in the development of cryo-EM has happened less than a decade ago, with the introduction of direct detection devices. These cameras allow unprecedented speed and resolution, and Lawrence Berkeley National Lab is developing a new detector, the 4D cam- era, that can operate at 87000 frames per second, revealing exclusive temporal dynamics of the investigated processes.
The current bottlenecks of the 4D camera, however, are the management of the large amount of data generated (around 50 GB/s) and the intrinsic noise level characterizing the signal acquired at that speed. Yet, the high frame rate enables the recognition of single electrons when they strike the detector, as opposed to traditional electron microscopy, where the charge is cumulated for every frame. Electron counting has remarkable advantages since it completely rejects electrical background noise as well as the variability in the electron charge deposition phenomena and it dramatically compresses images by saving them as lists of events coordinates.
With this work, the counting efficiency of the algorithm is enhanced, through the introduction of a denoising step before thresholding out the background noise, rising the precision by 7.11% with respect to the reference implementation. Furthermore, the localization of the events is refined to allow super-resolution, and a classification step is added to reduce the is- sue of collision losses, caused by overlapping electrons. In the end, a 10000x compression ratio is achieved thanks to electron counting. A GPU acceleration of the final algorithm is also proposed, achieving, in the best case, a speed up of 284x. The timing performances of the developed tool, in fact, are crucial for its real time execution on the microscope output.
Ultimately, this work aims at enabling a more efficient data management between the microscopy center and the supercomputing facility, both involved in the data processing pipeline, by moving part of the computation towards the instrumentation and transferring only a compressed version of the datasets. The intelligent redistribution of workloads, in fact, removes the bottleneck in data transfer and grants the use of the microscope at its maximum frame rate.ECCO: An Electron Counting Implementation for Image Compression and Optimizat...
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- Edoardo Ramalli, M.Sc. student in Computer Science and Engineering
Drug Repurposing is the investigation of existing drugs on the pharmaceutical market for new therapeutic purposes; drug repurposing reduces the time and cost of clinical trial steps, saving years, and billions of dollars in R&D. Identifying new diseases on which a drug can be effective is a complex problem: our approach leverages knowledge graphs (KG), networks composed of many types of entities and relations, on which embedding and graph completion techniques can be applied to infer insights and analyses. Our KG is built from well-known databases such as DrugBank, UniProt, and CTD and contains over one million relationships between more than 70K biological and pharmaceutical entities like diseases, genes, proteins and drugs. In this work, we research the applicability of knowledge graph completion techniques, such as link prediction (and triple classification) using a various number of different embedding models from different families: matrix factorization, geometric and Deep learning. Using these models is possible to infer new drug-disease relationships on our KG, and identify novel drug repurposing candidates. Preliminary experimental results are encouraging and show how state-of-the-art machine learning models, combined with the ever-growing amount of biological data freely available to the research community, could significantly improve the field of drug repurposing.Embedding based knowledge graph link prediction for drug repurposing
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- Daniele Valentino de Vincenti, B.Sc. graduate in Biomedical Engineering @Politecnico di Milano
- Lorenzo Farinelli, B.Sc. graduate in Computer Science and Engineering @Politecnico di Milano
Plaster is a multi-layered infrastructure (based on C++) aimed at supporting the development of multi-FPGA systems and the management of large data flows between the nodes. In particular, the goal of the project is to provide the end-user with a set of tools (by the means of a Python library and a C++ service) to easily assign bitstreams to nodes and route data between them, in the context of a PYNQ-based cluster suitable for distributed acceleration of computation-intensive tasks. Using this platform, an abandoned objects detection tool is implemented, designed as a Multi-FPGA distributed system exploiting an hardware accelerated version of the YOLO neural network for image detection.PLASTER - PYNQ-based abandoned object detection using a map-reduce approach o...
PLASTER - PYNQ-based abandoned object detection using a map-reduce approach o...NECST Lab @ Politecnico di Milano
- Jessica Leoni, PhD student in Data Analysis and Decision Science @Politecnico di Milano
- Luca Stornaiuolo, PhD student in Computer Science @Politecnico di Milano EMPhASIS - An EMbedded Public Attention Stress Identification System
EMPhASIS - An EMbedded Public Attention Stress Identification SystemNECST Lab @ Politecnico di Milano
Il secondo semestre dell’A.A. 2019/20 non lo scorderemo tanto facilmente: nel momento di pausa tra la fine degli appelli d’esame e la ripartenza delle lezioni il mondo ci è cambiato sotto gli occhi. All’improvviso abbiamo dovuto reinventarci un modo per erogare la didattica del secondo semestre, per consentire ai nostri ragazzi di laurearsi, per far loro sostenere gli esami. In poco meno di due settimane il nostro Ateneo è riuscito a convertire da didattica in presenza a online circa 1400 insegnamenti: una massiccia prova di resilienza e adattamento al cambiamento. Non è stato facile, ha presentato molti problemi, ma è anche una straordinaria opportunità che adesso dobbiamo imparare a cogliere, per non perdere quanto di buono (ed è parecchio) è stato fatto in questi mesi complicati. Mesi in cui, per citare lo storico Yuval Harari decisioni che in tempi normali richiederebbero anni di attenta valutazione sono state approvate nel giro di poche ore.NECSTTechTalk: La didattica del Politecnico di Milano (e non solo!) ai tempi ...
NECSTTechTalk: La didattica del Politecnico di Milano (e non solo!) ai tempi ...NECST Lab @ Politecnico di Milano
Abstract inglese:
In recent years Graphic Processing Units have seen widespread adoption in many scientific fields, from Machine Learning (ML) to Genomics. Their use makes it possible to achieve significant speedups and improvements in power efficiency over computationally intensive algorithms compared to General Purpose Central Processing Units. However, algorithms require specific knowledge of the GPU architecture and expertise to achieve significant results. In this work, we describe a methodology for automatic GPU kernel optimization. Our methodology exploits the Berkeley Roofline Model to perform a performance analysis of the algorithm considered and aims to increase the accessibility of GPU programming automatizing the optimization process of the kernel. We provide an in-depth analysis of this methodology, an overview on the state of the art, and a description of a tool we developed that automatically applies our methodology to obtain a highly optimized GPU version of two of the most popular algorithms used in computational biology, the X-drop and Smith-Waterman algorithms. The Smith-Waterman algorithm is one of the most used algorithms in genomics pipelines. The algorithm finds the optimal local alignment between two genomic sequences, at the cost of being particularly compute-intensive. The popular X-drop algorithm reduces the time required by the alignment by searching only for high-quality alignments. The algorithms accelerated using our methodology achieve more than 6x and 3x speed-up, for the X-drop and Smith-Waterman algorithms respectively, with respect to the state of the art implementation of these algorithms.A Methodology for Automatic GPU Kernel Optimization - NECSTTechTalk 4/06/2020
A Methodology for Automatic GPU Kernel Optimization - NECSTTechTalk 4/06/2020NECST Lab @ Politecnico di Milano
Outstanding advancements in imaging technology have made cryogenic electron microscopy a powerful technique for the nanocharacterization of biological macromolecular complexes, reaching atomic levels of resolution and being applicable to a wider set of samples than the other competing technologies. The real breakthrough in the development of cryo-EM has happened less than a decade ago, with the introduction of direct detection devices. These cameras allow unprecedented speed and resolution, and Lawrence Berkeley National Lab is developing a new detector, the 4D cam- era, that can operate at 87000 frames per second, revealing exclusive temporal dynamics of the investigated processes.
The current bottlenecks of the 4D camera, however, are the management of the large amount of data generated (around 50 GB/s) and the intrinsic noise level characterizing the signal acquired at that speed. Yet, the high frame rate enables the recognition of single electrons when they strike the detector, as opposed to traditional electron microscopy, where the charge is cumulated for every frame. Electron counting has remarkable advantages since it completely rejects electrical background noise as well as the variability in the electron charge deposition phenomena and it dramatically compresses images by saving them as lists of events coordinates.
With this work, the counting efficiency of the algorithm is enhanced, through the introduction of a denoising step before thresholding out the background noise, rising the precision by 7.11% with respect to the reference implementation. Furthermore, the localization of the events is refined to allow super-resolution, and a classification step is added to reduce the is- sue of collision losses, caused by overlapping electrons. In the end, a 10000x compression ratio is achieved thanks to electron counting. A GPU acceleration of the final algorithm is also proposed, achieving, in the best case, a speed up of 284x. The timing performances of the developed tool, in fact, are crucial for its real time execution on the microscope output.
Ultimately, this work aims at enabling a more efficient data management between the microscopy center and the supercomputing facility, both involved in the data processing pipeline, by moving part of the computation towards the instrumentation and transferring only a compressed version of the datasets. The intelligent redistribution of workloads, in fact, removes the bottleneck in data transfer and grants the use of the microscope at its maximum frame rate.ECCO: An Electron Counting Implementation for Image Compression and Optimizat...
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TreeHouse, nourish your community
- 1. TREEHOUSE Web App and Widget 1vNGC2020 19.07.2020
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- 5. 5 COMMUNITY
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- 7. 7 VERSATILE CROSS PLATFORM INTUITIVE
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- 27. SILVIA BREMBATI PRODUCT DESIGNER silvia.brembati@mail.polimi.it BENEDETTA BOLIS ENGINEERING PHYSICS STUDENT benedetta.bolis@mail.polimi.it virtualNGC 2020