- The document describes running a GPU burst simulation for IceCube astrophysics research across 50,000 NVIDIA GPUs in multiple cloud platforms globally, achieving 350 petaflops for 2 hours.
- IceCube detects high-energy neutrinos to study violent astrophysical events by observing the interactions of neutrinos within a cubic kilometer of Antarctic ice instrumented with sensors.
- The GPU burst simulation campaign helped improve IceCube's ability to reconstruct neutrino direction and energy and identify astrophysical sources through multi-messenger astrophysics.
This is the keynote talk fkw gave at cloudnet 2020. It covers all three cloudbursts we did. As of early 2021, slides 26ff is still the most detailed documentation of the 3rd cloudburst. This material will be covered in a future conference paper.
"Building and running the cloud GPU vacuum cleaner"Frank Wuerthwein
This talk, describing the "Largest Cloud Simulation in History" (Jensen Huang at SC19), was given at the MAGIC meeting on Dec. 4th 2019. MAGIC stands for "Middleware and Grid Interagency Cooperation", and is a group within NITRD. Current federal agencies that are members of MAGIC include DOC, DOD, DOE, HHS, NASA, and NSF.
Running a GPU burst for Multi-Messenger Astrophysics with IceCube across all ...Igor Sfiligoi
The San Diego Supercomputer Center (SDSC) and the Wisconsin IceCube Particle Astrophysics Center (WIPAC) at the University of Wisconsin–Madison successfully completed a computational experiment as part of a multi-institution collaboration that marshalled all globally available for sale GPUs (graphics processing units) across Amazon Web Services, Microsoft Azure, and the Google Cloud Platform.
In all, some 51,500 GPU processors were used during the approximately 2-hour experiment conducted on November 16 and funded under a National Science Foundation EAGER grant.
The experiment – completed just prior to the opening of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19) in Denver, CO – was coordinated by Frank Würthwein, SDSC Lead for High-Throughput Computing, and Benedikt Riedel, Computing Manager for the IceCube Neutrino Observatory and Global Computing Coordinator at WIPAC. Igor Sfiligoi, SDSC’s lead scientific software developer for high-throughput computing, and David Schultz, a production software manager with IceCube, conducted the actual run.
This presentation was given at several booths during SC19 by Frank Würthwein.
NRP Engagement webinar - Running a 51k GPU multi-cloud burst for MMA with Ic...Igor Sfiligoi
NRP Engagement webinar: Description of the 380 PFLOP32S , 51k GPU multi-cloud burst using HTCondor to run IceCube photon propagation simulation.
Presented January 27th, 2020.
Burst data retrieval after 50k GPU Cloud runIgor Sfiligoi
We ran a 50k GPU multi-cloud simulation to support the IceCube science. This talk provided an overview of what happened to the associated data.
Presented at the Internet2 booth at SC19.
For IceCube, large amount of photon propagation simulation is needed to properly calibrate natural Ice. Simulation is compute intensive and ideal for GPU compute. This Cloud run was more data intensive than precious ones, producing 130 TB of output data. To keep egress costs in check, we created dedicated network links via the Internet2 Cloud Connect Service.
Demonstrating a Pre-Exascale, Cost-Effective Multi-Cloud Environment for Scie...Igor Sfiligoi
Presented at PEARC20.
This talk presents expanding the IceCube’s production HTCondor pool using cost-effective GPU instances in preemptible mode gathered from the three major Cloud providers, namely Amazon Web Services, Microsoft Azure and the Google Cloud Platform. Using this setup, we sustained for a whole workday about 15k GPUs, corresponding to around 170 PFLOP32s, integrating over one EFLOP32 hour worth of science output for a price tag of about $60k. In this paper, we provide the reasoning behind Cloud instance selection, a description of the setup and an analysis of the provisioned resources, as well as a short description of the actual science output of the exercise.
This is the keynote talk fkw gave at cloudnet 2020. It covers all three cloudbursts we did. As of early 2021, slides 26ff is still the most detailed documentation of the 3rd cloudburst. This material will be covered in a future conference paper.
"Building and running the cloud GPU vacuum cleaner"Frank Wuerthwein
This talk, describing the "Largest Cloud Simulation in History" (Jensen Huang at SC19), was given at the MAGIC meeting on Dec. 4th 2019. MAGIC stands for "Middleware and Grid Interagency Cooperation", and is a group within NITRD. Current federal agencies that are members of MAGIC include DOC, DOD, DOE, HHS, NASA, and NSF.
Running a GPU burst for Multi-Messenger Astrophysics with IceCube across all ...Igor Sfiligoi
The San Diego Supercomputer Center (SDSC) and the Wisconsin IceCube Particle Astrophysics Center (WIPAC) at the University of Wisconsin–Madison successfully completed a computational experiment as part of a multi-institution collaboration that marshalled all globally available for sale GPUs (graphics processing units) across Amazon Web Services, Microsoft Azure, and the Google Cloud Platform.
In all, some 51,500 GPU processors were used during the approximately 2-hour experiment conducted on November 16 and funded under a National Science Foundation EAGER grant.
The experiment – completed just prior to the opening of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19) in Denver, CO – was coordinated by Frank Würthwein, SDSC Lead for High-Throughput Computing, and Benedikt Riedel, Computing Manager for the IceCube Neutrino Observatory and Global Computing Coordinator at WIPAC. Igor Sfiligoi, SDSC’s lead scientific software developer for high-throughput computing, and David Schultz, a production software manager with IceCube, conducted the actual run.
This presentation was given at several booths during SC19 by Frank Würthwein.
NRP Engagement webinar - Running a 51k GPU multi-cloud burst for MMA with Ic...Igor Sfiligoi
NRP Engagement webinar: Description of the 380 PFLOP32S , 51k GPU multi-cloud burst using HTCondor to run IceCube photon propagation simulation.
Presented January 27th, 2020.
Burst data retrieval after 50k GPU Cloud runIgor Sfiligoi
We ran a 50k GPU multi-cloud simulation to support the IceCube science. This talk provided an overview of what happened to the associated data.
Presented at the Internet2 booth at SC19.
For IceCube, large amount of photon propagation simulation is needed to properly calibrate natural Ice. Simulation is compute intensive and ideal for GPU compute. This Cloud run was more data intensive than precious ones, producing 130 TB of output data. To keep egress costs in check, we created dedicated network links via the Internet2 Cloud Connect Service.
Demonstrating a Pre-Exascale, Cost-Effective Multi-Cloud Environment for Scie...Igor Sfiligoi
Presented at PEARC20.
This talk presents expanding the IceCube’s production HTCondor pool using cost-effective GPU instances in preemptible mode gathered from the three major Cloud providers, namely Amazon Web Services, Microsoft Azure and the Google Cloud Platform. Using this setup, we sustained for a whole workday about 15k GPUs, corresponding to around 170 PFLOP32s, integrating over one EFLOP32 hour worth of science output for a price tag of about $60k. In this paper, we provide the reasoning behind Cloud instance selection, a description of the setup and an analysis of the provisioned resources, as well as a short description of the actual science output of the exercise.
Using A100 MIG to Scale Astronomy Scientific OutputIgor Sfiligoi
Presented at GTC21.
The raw computing power of GPUs has been steadily increasing, significantly outpacing the CPU gains. This poses a problem for many GPU-enabled scientific applications that use CPU code paths to feed data to the GPU code, resulting in lower GPU utilization, and thus reduced gains in scientific output. Applications that are high-throughput in nature, such as astronomy-focused IceCube and LIGO, can partially work around the problem by running several instances of the executable on the same GPU. This approach, however, is sub-optimal both in terms of application performance and workflow management complexity. The recently introduced Multi-Instance GPU (MIG) capability, available on the NVIDIA A100 GPU, provides a much cleaner and easier-to-use alternative by allowing the logical slicing of the powerful GPU and assigning different slices to different applications. And at least in the case of IceCube, it can provide over 3x more scientific output on the same hardware.
A Campus-Scale High Performance Cyberinfrastructure is Required for Data-Int...Larry Smarr
11.12.12
Seminar Presentation
Princeton Institute for Computational Science and Engineering (PICSciE)
Princeton University
Title: A Campus-Scale High Performance Cyberinfrastructure is Required for Data-Intensive Research
Princeton, NJ
In this slidecast, Jason Stowe from Cycle Computing describes the company's recent record-breaking Petascale CycleCloud HPC production run.
"For this big workload, a 156,314-core CycleCloud behemoth spanning 8 AWS regions, totaling 1.21 petaFLOPS (RPeak, not RMax) of aggregate compute power, to simulate 205,000 materials, crunched 264 compute years in only 18 hours. Thanks to Cycle's software and Amazon's Spot Instances, a supercomputing environment worth $68M if you had bought it, ran 2.3 Million hours of material science, approximately 264 compute-years, of simulation in only 18 hours, cost only $33,000, or $0.16 per molecule."
Learn more: http://blog.cyclecomputing.com/2013/11/back-to-the-future-121-petaflopsrpeak-156000-core-cyclecloud-hpc-runs-264-years-of-materials-science.html
Watch the video presentation: http://wp.me/p3RLHQ-aO9
Differential data processing for energy efficiency of wireless sensor networksDaniel Lim
Wireless sensor networks use many types of wireless sensors to configure network. However batteries in wireless sensor nodes are energy limited and consume considerable energy for data transmission. Therefore, data merging is used as a means to increase energy efficiency in data transmission. In this paper, we propose Differential Data Processing(DDP), which reduces the size of data transmitted from the sensor node to increase the energy efficiency of the wireless sensor network. Experimental results show that processing the differential temperature data reduces the average data size of the sensor node by 30%.
How to Prepare Weather and Climate Models for Future HPC Hardwareinside-BigData.com
In this video from GTC 2018, Peter Dueben from ECMWF presents: How to Prepare Weather and Climate Models for Future HPC Hardware.
"Learn how one of the leading institutes for global weather predictions, the European Centre for Medium-Range Weather Forecasts (ECMWF), is preparing for exascale supercomputing and the efficient use of future HPC computing hardware. I will name the main reasons why it is difficult to design efficient weather and climate models and provide an overview on the ongoing community effort to achieve the best possible model performance on existing and future HPC architectures. I will present the EU H2020 projects ESCAPE and ESiWACE and discuss recent approaches to increase computing performance in weather and climate modeling such as the use of reduced numerical precision and deep learning."
Watch the video: https://wp.me/p3RLHQ-ixu
Learn more: https://www.ecmwf.int/
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Making Sense of Information Through Planetary Scale ComputingLarry Smarr
09.03.01
Invited Presentation to the
Diamond Exchange—Brave New World
Title: Making Sense of Information Through Planetary Scale Computing
Monterey, CA
In this video from the DDN User Group Meeting at SC14, Steve Simms from Indiana University presents: Indiana University's Data Capacitor II.
"The High Performance File Systems unit of UITSResearch Technologies operates two separate high-speed file systems for temporary storage of research data. Both use the open sourceLustre parallel distributed file system running on a version of theLinux operating system: Data Capacitor II (DC2) is a larger, faster replacement for the former Data Capacitor, which was decommissioned January 7, 2014. Like its predecessor, DC2 is a large-capacity, high-throughput, high-bandwidth Lustre-based file system serving all IU campuses. It is mounted on the Big Red II, Karst,Quarry, and Mason research computing systems."
Toward a Global Interactive Earth Observing CyberinfrastructureLarry Smarr
05.01.12
Invited Talk to the 21st International Conference on Interactive Information Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology Held at the 85th AMS Annual Meeting
Title: Toward a Global Interactive Earth Observing Cyberinfrastructure
San Diego, CA
Science and Cyberinfrastructure in the Data-Dominated EraLarry Smarr
10.02.22
Invited talk
Symposium #1610, How Computational Science Is Tackling the Grand Challenges Facing Science and Society
Title: Science and Cyberinfrastructure in the Data-Dominated Era
San Diego, CA
Using A100 MIG to Scale Astronomy Scientific OutputIgor Sfiligoi
Presented at GTC21.
The raw computing power of GPUs has been steadily increasing, significantly outpacing the CPU gains. This poses a problem for many GPU-enabled scientific applications that use CPU code paths to feed data to the GPU code, resulting in lower GPU utilization, and thus reduced gains in scientific output. Applications that are high-throughput in nature, such as astronomy-focused IceCube and LIGO, can partially work around the problem by running several instances of the executable on the same GPU. This approach, however, is sub-optimal both in terms of application performance and workflow management complexity. The recently introduced Multi-Instance GPU (MIG) capability, available on the NVIDIA A100 GPU, provides a much cleaner and easier-to-use alternative by allowing the logical slicing of the powerful GPU and assigning different slices to different applications. And at least in the case of IceCube, it can provide over 3x more scientific output on the same hardware.
A Campus-Scale High Performance Cyberinfrastructure is Required for Data-Int...Larry Smarr
11.12.12
Seminar Presentation
Princeton Institute for Computational Science and Engineering (PICSciE)
Princeton University
Title: A Campus-Scale High Performance Cyberinfrastructure is Required for Data-Intensive Research
Princeton, NJ
In this slidecast, Jason Stowe from Cycle Computing describes the company's recent record-breaking Petascale CycleCloud HPC production run.
"For this big workload, a 156,314-core CycleCloud behemoth spanning 8 AWS regions, totaling 1.21 petaFLOPS (RPeak, not RMax) of aggregate compute power, to simulate 205,000 materials, crunched 264 compute years in only 18 hours. Thanks to Cycle's software and Amazon's Spot Instances, a supercomputing environment worth $68M if you had bought it, ran 2.3 Million hours of material science, approximately 264 compute-years, of simulation in only 18 hours, cost only $33,000, or $0.16 per molecule."
Learn more: http://blog.cyclecomputing.com/2013/11/back-to-the-future-121-petaflopsrpeak-156000-core-cyclecloud-hpc-runs-264-years-of-materials-science.html
Watch the video presentation: http://wp.me/p3RLHQ-aO9
Differential data processing for energy efficiency of wireless sensor networksDaniel Lim
Wireless sensor networks use many types of wireless sensors to configure network. However batteries in wireless sensor nodes are energy limited and consume considerable energy for data transmission. Therefore, data merging is used as a means to increase energy efficiency in data transmission. In this paper, we propose Differential Data Processing(DDP), which reduces the size of data transmitted from the sensor node to increase the energy efficiency of the wireless sensor network. Experimental results show that processing the differential temperature data reduces the average data size of the sensor node by 30%.
How to Prepare Weather and Climate Models for Future HPC Hardwareinside-BigData.com
In this video from GTC 2018, Peter Dueben from ECMWF presents: How to Prepare Weather and Climate Models for Future HPC Hardware.
"Learn how one of the leading institutes for global weather predictions, the European Centre for Medium-Range Weather Forecasts (ECMWF), is preparing for exascale supercomputing and the efficient use of future HPC computing hardware. I will name the main reasons why it is difficult to design efficient weather and climate models and provide an overview on the ongoing community effort to achieve the best possible model performance on existing and future HPC architectures. I will present the EU H2020 projects ESCAPE and ESiWACE and discuss recent approaches to increase computing performance in weather and climate modeling such as the use of reduced numerical precision and deep learning."
Watch the video: https://wp.me/p3RLHQ-ixu
Learn more: https://www.ecmwf.int/
and
https://www.nvidia.com/en-us/gtc/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Making Sense of Information Through Planetary Scale ComputingLarry Smarr
09.03.01
Invited Presentation to the
Diamond Exchange—Brave New World
Title: Making Sense of Information Through Planetary Scale Computing
Monterey, CA
In this video from the DDN User Group Meeting at SC14, Steve Simms from Indiana University presents: Indiana University's Data Capacitor II.
"The High Performance File Systems unit of UITSResearch Technologies operates two separate high-speed file systems for temporary storage of research data. Both use the open sourceLustre parallel distributed file system running on a version of theLinux operating system: Data Capacitor II (DC2) is a larger, faster replacement for the former Data Capacitor, which was decommissioned January 7, 2014. Like its predecessor, DC2 is a large-capacity, high-throughput, high-bandwidth Lustre-based file system serving all IU campuses. It is mounted on the Big Red II, Karst,Quarry, and Mason research computing systems."
Toward a Global Interactive Earth Observing CyberinfrastructureLarry Smarr
05.01.12
Invited Talk to the 21st International Conference on Interactive Information Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology Held at the 85th AMS Annual Meeting
Title: Toward a Global Interactive Earth Observing Cyberinfrastructure
San Diego, CA
Science and Cyberinfrastructure in the Data-Dominated EraLarry Smarr
10.02.22
Invited talk
Symposium #1610, How Computational Science Is Tackling the Grand Challenges Facing Science and Society
Title: Science and Cyberinfrastructure in the Data-Dominated Era
San Diego, CA
NASA Advanced Computing Environment for Science & Engineeringinside-BigData.com
In this deck from the 2017 Argonne Training Program on Extreme-Scale Computing, Rupak Biswas from NASA presents: NASA Advanced Computing Environment for Science & Engineering.
""High performance computing is now integral to NASA’s portfolio of missions to pioneer the future of space exploration, accelerate scientific discovery, and enable aeronautics research. Anchored by the Pleiades supercomputer at NASA Ames Research Center, the High End Computing Capability (HECC) Project provides a fully integrated environment to satisfy NASA’s diverse modeling, simulation, and analysis needs. In addition, HECC serves as the agency’s expert source for evaluating emerging HPC technologies and maturing the most appropriate ones into the production environment. This includes investigating advanced IT technologies such as accelerators, cloud computing, collaborative environments, big data analytics, and adiabatic quantum computing. The overall goal is to provide a consolidated bleeding-edge environment to support NASA's computational and analysis requirements for science and engineering applications."
Dr. Rupak Biswas is currently the Director of Exploration Technology at NASA Ames Research Center, Moffett Field, Calif., and has held this Senior Executive Service (SES) position since January 2016. In this role, he in charge of planning, directing, and coordinating the technology development and operational activities of the organization that comprises of advanced supercomputing, human systems integration, intelligent systems, and entry systems technology. The directorate consists of approximately 700 employees with an annual budget of $160 million, and includes two of NASA’s critical and consolidated infrastructures: arc jet testing facility and supercomputing facility. He is also the Manager of the NASA-wide High End Computing Capability Project that provides a full range of advanced computational resources and services to numerous programs across the agency. In addition, he leads the emerging quantum computing effort for NASA.
Watch the video: https://wp.me/p3RLHQ-hua
Learn more: https://extremecomputingtraining.anl.gov/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
From pixels to point clouds - Using drones,game engines and virtual reality t...ARDC
Presentation by Dr Tim Brown
Full webinar: https://www.youtube.com/watch?v=bl_7ClXhQlA&list=PLG25fMbdLRa5qsPiBGPaj2NHqPyG8X435&index=11
Individual snippet:https://youtu.be/PVf4zYNJlmM?list=PLG25fMbdLRa5qsPiBGPaj2NHqPyG8X435
Frossie Economou & Angelo Fausti [Vera C. Rubin Observatory] | How InfluxDB H...InfluxData
Frossie Economou & Angelo Fausti [Vera C. Rubin Observatory] | How InfluxDB Helps Vera C. Rubin Observatory Make the Deepest, Widest Image of the Universe | InfluxDays Virtual Experience NA 2020
Statistical estimation and inference for large data sets require computationally efficient optimization methods. Remote sensing retrievals are, in fact, estimates of the underlying true state, and their optimization routines must necessarily make compromises in order to keep up with large data volumes. A sub-group of the Remote Sensing Working Group of the SAMSI Program on Mathematical and Statistical Methods for Climate and the Earth System is investigating how optimization in Bayesian-inspired retrievals and o_-line statistical methods could be made more computationally efficient. We will report on discussions held to-date and describe how progress in the theory of data systems research can positively impact optimization methodologies.
Project StarGate An End-to-End 10Gbps HPC to User Cyberinfrastructure ANL * C...Larry Smarr
09.11.03
Report to the
Dept. of Energy Advanced Scientific Computing Advisory Committee
Title: Project StarGate An End-to-End 10Gbps HPC to User Cyberinfrastructure ANL * Calit2 * LBNL * NICS * ORNL * SDSC
Oak Ridge, TN
LambdaGrids--Earth and Planetary Sciences Driving High Performance Networks a...Larry Smarr
05.02.04
Invited Talk to the NASA Jet Propulsion Laboratory
Title: LambdaGrids--Earth and Planetary Sciences Driving High Performance Networks and High Resolution Visualizations
Pasadena, CA
High Performance Cyberinfrastructure is Needed to Enable Data-Intensive Scien...Larry Smarr
11.03.28
Remote Luncheon Presentation from Calit2@UCSD
National Science Board
Expert Panel Discussion on Data Policies
National Science Foundation
Title: High Performance Cyberinfrastructure is Needed to Enable Data-Intensive Science and Engineering
Arlington, Virginia
Applying Photonics to User Needs: The Application ChallengeLarry Smarr
05.02.28
Invited Talk to the 4th Annual On*VECTOR International Photonics Workshop
Sponsored by NTT Network Innovation Laboratories
Title: Applying Photonics to User Needs: The Application Challenge
University of California, San Diego
The Academic and R&D Sectors' Current and Future Broadband and Fiber Access N...Larry Smarr
05.02.23
Invited Access Grid Talk
MSCMC FORUM Series
Examining the National Vision for Global Peace and Prosperity
Title: The Academic and R&D Sectors' Current and Future Broadband and Fiber Access Needs for US Global Competitiveness
Arlington, VA
The Jump to Light Speed - Data Intensive Earth Sciences are Leading the Way t...Larry Smarr
05.06.14
Keynote to the 15th Federation of Earth Science Information Partners Assembly Meeting: Linking Data and Information to Decision Makers
Title: The Jump to Light Speed - Data Intensive Earth Sciences are Leading the Way to the International LambdaGrid
San Diego, CA
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Running a GPU burst for Multi-Messenger Astrophysics with IceCube across all available GPUs in the Cloud
1. Running a GPU burst for Multi-
Messenger Astrophysics with
IceCube across all available GPUs in
the Cloud
Frank Würthwein
OSG Executive Director
UCSD/SDSC
2. Jensen Huang keynote @ SC19
2
The Largest Cloud Simulation in History
50k NVIDIA GPUs in the Cloud
350 Petaflops for 2 hours
Distributed across US, Europe & Asia
Saturday morning before SC19 we bought all GPU capacity that was for sale in
Amazon Web Services, Microsoft Azure, and Google Cloud Platform worldwide
4. IceCube
4
A cubic kilometer of ice at the
south pole is instrumented
with 5160 optical sensors.
Astrophysics:
• Discovery of astrophysical neutrinos
• First evidence of neutrino point source (TXS)
• Cosmic rays with surface detector
Particle Physics:
• Atmospheric neutrino oscillation
• Neutrino cross sections at TeV scale
• New physics searches at highest energies
Earth Science:
• Glaciology
• Earth tomography
A facility with very
diverse science goals
Restrict this talk to
high energy Astrophysics
5. High Energy Astrophysics Science
case for IceCube
5
Universe is opaque to light
at highest energies and
distances.
Only gravitational waves
and neutrinos can pinpoint
most violent events in
universe.
Fortunately, highest energy
neutrinos are of cosmic origin.
Effectively “background free” as long
as energy is measured correctly.
6. High energy neutrinos from
outside the solar system
6
First 28 very high energy neutrinos from outside the solar system
Red curve is the photon flux
spectrum measured with the
Fermi satellite.
Black points show the
corresponding high energy
neutrino flux spectrum
measured by IceCube.
This demonstrates both the opaqueness of the universe to high energy
photons, and the ability of IceCube to detect neutrinos above the maximum
energy we can see light due to this opaqueness.
Science 342 (2013). DOI:
10.1126/science.1242856
7. Understanding the Origin
7
We now know high energy events happen in the universe. What are they?
p + g D + p + p 0 p + gg
p + g D + n + p + n + m + n
Co
Aya Ishihara
The hypothesis:
The same cosmic events produce
neutrinos and photons
We detect the electrons or muons from neutrino that interact in the ice.
Neutrino interact very weakly => need a very large array of ice instrumented
to maximize chances that a cosmic neutrino interacts inside the detector.
Need pointing accuracy to point back to origin of neutrino.
Telescopes the world over then try to identify the source in the direction
IceCube is pointing to for the neutrino. Multi-messenger Astrophysics
8. The ν detection challenge
8
Optical Pro
Aya Ishiha
• Combining all the possible info
• These features are included in
• We’re always be developing th
Nature never tell us a perfec
satisfactory agreem
Ice properties change with
depth and wavelength
Observed pointing resolution at high
energies is systematics limited.
Central value moves
for different ice models
Improved e and τ reconstruction
increased neutrino flux
detection
more observations
Photon propagation through
ice runs efficiently on single
precision GPU.
Detailed simulation campaigns
to improve pointing resolution
by improving ice model.
Improvement in reconstruction with
better ice model near the detectors
9. First evidence of an origin
9
First location of a source of very high energy neutrinos.
Neutrino produced high energy muon
near IceCube. Muon produced light as it
traverses IceCube volume. Light is
detected by array of phototubes of
IceCube.
IceCube alerted the astronomy community of the
observation of a single high energy neutrino on
September 22 2017.
A blazar designated by astronomers as TXS
0506+056 was subsequently identified as most likely
source in the direction IceCube was pointing. Multiple
telescopes saw light from TXS at the same time
IceCube saw the neutrino.
Science 361, 147-151
(2018). DOI:10.1126/science.aat2890
10. IceCube’s Future Plans
10
| IceCube Upgrade and Gen2 | Summer Blot | TeVPA 2018
The IceCube-Gen2 Facility
Preliminary timeline
MeV- to EeV-scale physics
Surface array
High Energy
Array
Radio array
PINGU
IC86
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 … 2032
Today
Surface air shower
ConstructionR&D Design & Approval
IceCube Upgrade
IceCube Upgrade
Deployment
Near term:
add more phototubes to deep core to increase granularity of measurements.
Longer term:
• Extend instrumented
volume at smaller
granularity.
• Extend even smaller
granularity deep core
volume.
• Add surface array.
Improve detector for low & high energy neutrinos
12. The Idea
• Integrate all GPUs available for sale
worldwide into a single HTCondor pool.
use 28 regions across AWS, Azure, and Google
Cloud for a burst of a couple hours, or so.
• IceCube submits their photon propagation
workflow to this HTCondor pool.
we handle the input, the jobs on the GPUs, and
the output as a single globally distributed system.
12
Run a GPU burst relevant in scale
for future Exascale HPC systems.
13. A global HTCondor pool
• IceCube, like all OSG user communities, relies on
HTCondor for resource orchestration
This demo used the standard tools
• Dedicated HW setup
Avoid disruption of OSG production system
Optimize HTCondor setup for the spiky nature of the demo
multiple schedds for IceCube to submit to
collecting resources in each cloud region, then collecting from all
regions into global pool
13
15. Using native Cloud storage
• Input data pre-staged into native Cloud storage
Each file in one-to-few Cloud regions
some replication to deal with limited predictability of resources per region
Local to Compute for large regions for maximum throughput
Reading from “close” region for smaller ones to minimize ops
• Output staged back to region-local Cloud storage
• Deployed simple wrappers around Cloud native file
transfer tools
IceCube jobs do not need to customize for different Clouds
They just need to know where input data is available
(pretty standard OSG operation mode)
15
16. The Testing Ahead of Time
16
~250,000 single threaded jobs
run across 28 cloud regions
during 80 minutes.
Peak at 90,000
jobs running.
up to 60k jobs started in ~10min.
Regions across US, EU, and
Asia were used in this test.
Demonstrated burst capability
of our infrastructure on CPUs.
Want scale of GPU burst to be limited
only by # of GPUs available for sale.
17. Science with 51,000 GPUs
achieved as peak performance
17
Time in Minutes
Each color is a different
cloud region in US, EU, or Asia.
Total of 28 Regions in use.
Peaked at 51,500 GPUs
~380 Petaflops of fp32
8 generations of NVIDIA GPUs used.
Summary of stats at peak
18. A Heterogenous Resource Pool
18
28 cloud Regions across 4 world regions
providing us with 8 GPU generations.
No one region or GPU type dominates!
19. Science Produced
19
Distributed High-Throughput
Computing (dHTC) paradigm
implemented via HTCondor provides
global resource aggregation.
Largest cloud region provided 10.8% of the total
dHTC paradigm can aggregate
on-prem anywhere
HPC at any scale
and multiple clouds
24. IceCube Input Segmentable
24
IceCube prepared two types of input files that differed
in x10 in the number of input events per file.
Small files processed by K80 and K520, large files by all other GPU types.
seconds seconds
A total of 10.2 Billion events were processed across ~175,000 GPU jobs.
Each job fetched a file from cloud storage to local storage, processed that file, and wrote
the output to cloud storage. For ¼ of the regions cloud storage was not local to the
region. => we could have probably avoided data replication across regions given the
excellent networking between regions for each provider.
25. Annual IceCube GPU use via OSG
25
Peaked at ~3000 GPUs for a day.
Last 12 months
The routine operations of IceCube is
as globally distributed as this cloud burst.
We produced ~3% of the annual photon
propagation simulations in this ~2h cloud burst.
26. Applicability beyond IceCube
• All the large instruments we know off
LHC, LIGO, DUNE, LSST, …
• Any midscale instrument we can think off
XENON, GlueX, Clas12, Nova, DES, Cryo-EM, …
• A large fraction of Deep Learning
But not all of it …
• Basically, anything that has bundles of
independently schedulable jobs that can be
partitioned to adjust workloads to have 0.5 to
few hour runtimes on modern GPUs.
26
27. Cost to support cloud as a “24x7”
capability
• Today, roughly $15k per 300 PFLOP32 hour
• This burst was executed by 2 people
Igor Sfiligoi (SDSC) to support the infrastructure.
David Schulz (UW Madison) to create and submit the
IceCube workflows.
David is needed also for on-prem science workflows.
• To make this a routine operations capability for any
open science that is dHTC capable would require
another 50% FTE “Cloud Budget Manager”.
There is substantial effort involved in just dealing with cost &
budgets for a large community of scientists.
27
28. IceCube is ready for Exascale
• Humanity has built extraordinary instruments by pooling
human and financial resources globally.
• The computing for these large collaborations fits perfectly to
the cloud or scheduling holes in Exascale HPC systems due
to its “ingeniously parallel” nature. => dHTC
• The dHTC computing paradigm applies to a wide range of
problems across all of open science.
We are happy to repeat this with anybody willing to spend $50k in the
clouds.
28
Contact us at: help@opensciencegrid.org
Or me personally at: fkw@ucsd.edu
Demonstrated elastic burst at 51,500 GPUs
IceCube is ready for Exascale
29. Acknowledgements
• This work was partially supported by the
NSF grants OAC-1941481, MPS-1148698,
OAC-1841530, and OAC-1826967
29