Bring High Performance Computing to Everyone in College of Engineering at Texas A&M University!
The Texas A&M Institute of Data Science invites you to attend the TAMIDS Data Science Webinars from Apr 1 to Apr 15, 2020. This webinar series is to introduce the fundamentals of data science (with python) to students and researchers from the Texas A&M University system.
Registration: FREE
More information of the webinars can be found at
March 18 IDEAS-ECP Webinar: Testing Strategies
April 20-24 OpenMP Hackathon
April 21-24 Kokkos Bootcamp and Training
Other Training Activities of Interest
March 9 NVIDIA Profiling Tools – Nsight Systems
March 10 NVIDIA Profiling Tools – Nsight Compute
March 25 DAOS: Next-Generation Data Management for Exascale
April/May/June OpenACC Series
May 5-7 2020 ALCF Computational Performance Workshop
2020 Monthly Series CUDA Training Series
2020 Series GPU Hackathon Series
More Information about Upcoming ECP Training Activities
IDEAS ECP Webinar: Testing Strategies when Learning Programming Models and Using High-Performance Libraries
March 18, 2020
URL: https://exascaleproject.org/event/testingstrategies/
The next webinar in the series is titled, Testing: Strategies When Learning Programming Models and Using High-Performance Libraries, and will be presented by Balint Joo (Jefferson Laboratory). The webinar will take place on Wednesday, March 18, 2020 at 1:00 pm ET.
Software testing is an invaluable practice, albeit the level of testing in scientific applications can vary widely, from no testing at all to full continuous integration (as discussed in earlier webinars of the HPC-BP series). In this webinar I will consider a specific case: the use of unit-testing when developing a mini-app as an approach to learn about new programming models such as Kokkos and SYCL, or when using (or contributing to) high-performance libraries. I will illustrate with an example from Lattice QCD, focusing on the integration of the QUDA optimized library with the Chroma application. The webinar will focus on lessons learned and generally applicable strategies.
For more information or to register, please visit the URL above.
OpenMP Hackathon at Georgia Tech
April 20-24, 2020
URL: https://sites.google.com/view/omp-hack-atl/home
The Electrical Engineering and Computer Science Department at Georgia Institute of Technology in conjunction with Oak Ridge National Laboratory (ORNL) is organizing an ECP OpenMP Hackathon on April 20–April 24, 2020. This event is sponsored by the Exascale Computing Project (ECP), and driven by the ECP SOLLVE Project. We encourage participation of teams especially interested in porting and optimizing their applications by using the latest OpenMP features.
For more information or to register, please visit the URL above.
Kokkos Bootcamp and Training
April 21-24, 2020
URL: https://www.olcf.ornl.gov/calendar/kokkos-bootcamp-and-training/
The Oak Ridge Leadership Computing Facility (OLCF) will host a Kokkos training event organized by ECP on April 21-24, 2020. This workshop is intended to teach new Kokkos users how to get started and to help existing Kokkos users to further improve their codes. The training will cover the minimum required topics to get your application started on using Kokkos, and Kokkos experts will be on hand to help the more advanced users. For more information or to register, please visit the URL above.
Other Events that Might be of Interest to ECP Project Teams
NVIDIA Profiling Tools – Nsight Systems
March 9, 2020
URL: https://www.olcf.ornl.gov/calendar/nvidia-profiling-tools-nsight-systems/
On March 9, 2020, NVIDIA will present a webinar on how to use NVIDIA’s Nsight Systems – a statistical sampling profiler with tracing features – on Summit. Nsight Systems and Nsight Compute are NVIDIA’s next-generation profiling tools for understanding and optimizing the performance of CUDA, OpenACC, or OpenMP applications. NVIDIA recommends transitioning to these new tools since nvprof and Visual profiler will be deprecated in a future CUDA release. The presentation will be delivered remotely, but there will be an in-person viewing of the webinar for participants with current ORNL badges. For more information or to register, please visit the URL above.
NVIDIA Profiling Tools – Nsight Compute
March 10, 2020
URL: https://www.olcf.ornl.gov/calendar/nvidia-profiling-tools-nsight-compute/
On March 10, 2020, NVIDIA will present a webinar on how to use NVIDIA’s Nsight Compute – a kernel-level analysis and performance metric tool – on Summit. Nsight Systems and Nsight Compute are NVIDIA’s next-generation profiling tools for understanding and optimizing the performance of CUDA, OpenACC, or OpenMP applications. NVIDIA recommends transitioning to these new tools since nvprof and Visual profiler will be deprecated in a future CUDA release. The presentation will be delivered remotely, but there will be an in-person viewing of the webinar for participants with current ORNL badges.
For more information or to register, please visit the URL above.
DAOS: Next Generation Data Management for Exascale
March 25, 2020
URL: https://www.alcf.anl.gov/events/daos-next-generation-data-management-exascale
The Argonne Leadership Computing Facility (ALCF) is presenting the next Aurora Early Adopter Series webinar on March 25, 2020. The Distributed Asynchronous Object Storage (DAOS) is an open-source, scale-out object store designed from the ground up for massively distributed Non-Volatile Memory (NVM). DAOS takes advantage of next-generation NVM technology, like Storage Class Memory (SCM) and NVM express (NVMe), and is extremely lightweight since it operates end-to-end in user space with full OS bypass. DAOS offers a shift away from an I/O model designed for block-based and high-latency storage to one that inherently supports fine-grained data access and unlocks the performance of the next-generation storage technologies. This presentation will introduce the key concepts behind DAOS and the software ecosystem enabling this technology. We will then provide more details on the DAOS deployment on Aurora and how applications will benefit from this new storage tier.
For more information or to register, please visit the URL above.
OpenACC Training Series
April 17, May 28, and June 23
URL: https://www.olcf.ornl.gov/openacc-training-series/
OpenACC is a directive-based approach to parallel programming for heterogeneous architectures, where developers specify regions of code (written in C, C++, and Fortran) to be offloaded from a host CPU to a GPU. This approach is meant to reduce the amount of programming effort required of developers relative to low-level models, such as CUDA.
NVIDIA will present a 3-part OpenACC training series intended to help new and existing GPU programmers learn to use the OpenACC API. Each part will include a 1-hour presentation and example exercises. The exercises are meant to reinforce the material from the presentation and can be completed during a 1-hour hands-on session following each lecture (for in-person participants) or on your own (for remote participants).
For more information or to register, please visit the URL above.
2020 ALCF Computational Performance Workshop
May 5-7, 2020
URL: https://www.alcf.anl.gov/events/2020-alcf-computational-performance-workshop
From May 5–7, 2020, the ALCF will host the annual ALCF Computational Performance Workshop to help researchers achieve computational readiness on ALCF computing resources. Workshop participants will have the opportunity to:
Work directly with ALCF and industry professionals during the workshop and dedicated hands-on sessions
Explore advanced techniques and tools to enhance code performance and expand your data science skills
Benchmark and debug your code with exclusive reservations on ALCF computing systems
Prepare for a major allocation award (e.g., INCITE, ALCC, ALCF Data Science Program)
For more information or to register, please visit the URL above.
CUDA Training Series
Monthly in 2020
URL: https://www.olcf.ornl.gov/cuda-training-series/
NVIDIA will present a 9-part CUDA training series intended to help new and existing GPU programmers understand the main concepts of the CUDA platform and its programming model. Each part will include a 1-hour presentation and example exercises. The exercises are meant to reinforce the material from the presentation and can be completed during a 1-hour hands-on session following each lecture (for in-person participants) or on your own (for remote participants). OLCF and NERSC will both be holding in-person events for each part of the series, where participants can watch the presentations and get help from experts during the hands-on sessions. In-person participants without current Summit or Cori-GPU access will be given temporary accounts to work on the examples.
For more information or to register, please visit the URL above.
2020 GPU Hackathon Series
URL: https://gpuhackathons.org/events
These GPU hackathons are 5-day coding events in which teams of developers prepare their own applications(s) to run on GPUs or focus on optimizing their application(s) that currently run on GPUs. Teams should consist of three or more developers who are intimately familiar with (some part of) their application, and they will work alongside two mentors with GPU programming expertise. If you want/need to get your code running (or optimized) on a GPU-accelerated system, these hackathons offer a unique opportunity to set aside 5 days, surround yourself with experts in the field, and push toward your goals.
Though its Data Science Trainee Program, TAMIDS helps faculty and researchers develop Data Science projects, match interested students to work on them, and provide technical guidance and mentorship during the project.
With the rapid development of information technologies, the applications of data science has dramatically changed various aspects of our society and economy. In responding to this emerging trend, the Texas A&M Institute of Data Science (TAMIDS) was established to pursue new approaches to data science research, education, operations and partnership at Texas A&M University.
Among all the objectives of TAMIDS, the education of the new generation of researchers that combine their disciplinary competences with data science expertise is of paramount importance. Through collaboration with A&M colleges, research agencies, and industrial partners, the TAMIDS Data Science Trainee Program breaks college boundaries to bring together the expertise and skills from science, engineering, operations, business, health, education, etc. to solve challenging problems while preparing our students for their future career success.
We identify three roles – Project Leader, Trainee, and Consultant – in which people can be involved in the program with TAMIDS.
Project Leaders:
Trainees:
Consultants:
TAMIDS will support and sustain the Data Science Traineeship Program by (1) managing the program organization; (2) assisting in matching leaders, projects and trainees; (3) organizing Data Science workshops and training programs; (4) helping with project computing and storage resources, and (5) helping develop Data Science courses and curriculum with domain emphases.
We invite A&M faculty, researchers, staff, and students to join this program to promote research, education, service, operations, and outreach in Data Science across Texas A&M. With your involvement and contribution, we will build a vibrant research and education environment for data science at Texas A&M.
For questions about the TAMIDS Data Science Traineeship Program, please contact Dr. Jian Tao, jtao@tamu.edu
Dr. Xia “Ben” Hu, Assistant Professor and Lynn ’84 & Bill Crane ’83 Faculty Fellow from the Dept. of Computer Science & Engineering, Texas A&M University will lead a tutorial workshop on Interpretable Machine Learning: Concepts and Techniques. The workshop will cover fundamental concepts as well as the state-of-the-art algorithms with implementations in interpretable machine learning.
February 21, 2020
2:00 pm – 5:00 pm
ETB 1005
More information about the workshop can be found at
https://tamids.tamu.edu/2020/02/09/workshop-interpretable-machine-learning/
Workshop background and topics
In many real-world applications, such as Healthcare and Cybersecurity, domain experts would appropriately trust and effectively manage prediction results only if they can understand the prediction model as well as results. For example, it is critical, for not only physicians but also patients, to understand why a patient is diagnosed with pre-diabetes and how the decision is made by a predictive model. This workshop will cover fundamental concepts as well as the state-of-the-art algorithms with implementations in interpretable machine learning. First, we will introduce the background of this problem, and widely used concepts and problem definition of interpretable machine learning. Second, we will discuss the system architecture and main algorithms, as well as our current progress, to bridge the gap between powerful deep learning algorithms and interpretable shallow models through model and application perspectives. At the end, we will introduce XDeep, which is an open-source Python package developed to interpret deep models for both practitioners and researchers. XDeep takes a trained deep neural network (DNN) as the input, and generates relevant interpretations as the output with the post-hoc manner. From the functionality perspective, XDeep integrates a wide range of interpretation algorithms from the state-of-the-arts, covering different types of methodologies, and is capable of providing both local explanation and global explanation for DNN when interpreting model behaviors.
Workshop organization
The first 1.5 hours of the tutorial is an introductory exposition of the topic. The second 1.5 hours is a practical session that helps the audience consolidate their knowledge through hands-on computational experience. Workshop participants should bring their own laptop.
Background knowledge advised for participants
Participants must have at least a basic knowledge of machine learning concepts from supervised and unsupervised learning. Some understanding of deep learning and frameworks (e.g. TensorFlow or PyTorch) would be helpful. Basic experience with Python programming on machine learning problems is assumed.
Workshop attendance and registration
Please register to attend the workshop at https://tinyurl.com/yx7b9u6e. Registration is free but is required due to space limitations.
Biography of the workshop leader
Dr. Xia “Ben” Hu is an Assistant Professor and Lynn ’84 and Bill Crane ’83 Faculty Fellow at Texas A&M University in the Department of Computer Science and Engineering. Hu directs the Data Analytics at Texas A&M (DATA) Lab. Dr. Hu has published over 100 papers in several major academic venues, including KDD, WWW, SIGIR, IJCAI, AAAI, etc. An open-source package developed by his group, namely AutoKeras, has become the most used automated deep learning system on Github (with over 6,000 stars and 1,000 forks). Also, his work on deep collaborative filtering, anomaly detection and knowledge graph have been included in the TensorFlow package, Apple production system and Bing production system, respectively. His papers have received several awards, including WWW 2019 Best Paper Shortlist, INFORMS 2019 Best Poster Award, INFORMS QSR 2019 Best Student Paper Finalist, IISE QCRE 2019 Best Student Paper Award, WSDM 2013 Best Paper Shortlist, IJCAI 2017 BOOM workshop Best Paper Award. He is the recipient of JP Morgan AI Faculty Award, Adobe Data Science Award, NSF CAREER Award, and ASU President Award for Innovation. His work has been featured in several news media, including the MIT Technology Review, ACM TechNews, New Scientist, Defense One, and others. Hu’s work has been cited more than 6,000 times with an h-index of 36. He was the conference General Co-Chair for WSDM 2020. More information can be found at http://faculty.cs.tamu.edu/xiahu/.
Further information
For more information about TAMIDS tutorial workshop series, please contact Ms. Jennifer South at jsouth@tamu.edu
The Texas A&M Institute of Data Science (TAMIDS), Texas A&M High Performance Research Computing (HPRC), and Texas Engineering Experiment Station invite you to join our Bring-Your-Own-Data (BYOD) workshop! The primary goal of this workshop is to help Aggie researchers take advantage of the latest data analytics technologies and Texas A&M high performance computing facilities to speed up various data science projects. This is a FREE service offered to all researchers at Texas A&M.
Time: Jan 21 – Apr 14, 2020 Tuesdays.
Location: 235-I Wisenbaker Engineering Building, 188 Bizzell St, College Station, Texas 77843
Please note that this is NOT a training session, but more in forms of a code development working meeting focusing exclusively on your project with a goal to overcome techinical hurdles you may have to move forward with your data science projects. Given the limited time and resources available to offer this workshop, you must be prepared with a determined mind to create substantial progress. We can only work on the software applications that you write and plan to maintain yourself or the applications that are built on top of an open source platform.
You are advised to bring your own laptop, but it is not required as long as you provide means to share your data with us at the workshop. Please feel free to contact the organizers if you have any questions or comments.
More about the BYOD workshop can be found here.
The Texas A&M Institute of Data Science invites you to attend the TAMIDS Data Science Webinars from Oct 15 to Oct 29, 2019. This webinar series is to introduce the fundamentals of data science (with python) to students and researchers from the Texas A&M University system.
Registration: FREE
More information of the webinars can be found at
FPGAs in the Era of AI and Big Data
by Lawrence Landis <lawrence.landis@intel.com>
102.B Student Computing Center, Texas A&M University
8:45AM – 5:00PM September 27th, 2019
Description:
Intel Programmable Logic Devices (FPGA) are used in a wide range of applications from industrial electronics, networking and AI acceleration. FPGAs are a staple of Electronic Engineering Curriculums due to their flexibility in describing electronic circuitry without requiring any semiconductor manufacturing tooling costs. Intel’s Programmable Solutions Group FPGA University Program engages with worldwide universities to promote FPGA education and research.
Learning Objectives:
Workshop Syllabus:
8:45 Registration
9:00: Lecture: FPGA applications and architecture, Quartus overview
10:00: Lab 1: Introduction to the Quartus Development Tool Suite using Verilog programming
11:15: Lecture: Embedded NIOS and Platform Designer
12:00: Lunch
1:00: Lab2: Embedded NIOS and Platform Designer
2:00: Lecture: High Level Design Usage Model for FPGAs – OpenCL and HLS
2:45: Lab3: High Level Design
3:30: Lecture: OpenVino overlay usage model
4:15: Lab4: OpenVino Vision and Neural Network Heterogeneous Computing
5:00 Conclude Workshop
Workshop Instructions:
The Quartus Lite free tool suite will be required for Labs 1 and 2. Please visit this site: http://fpgasoftware.intel.com/18.1/?edition=lite and install Quartus Prime and MAX 10 libraries.
With the support from the Texas A&M Institute of Data Science, the Texas Engineering Experiment Station, the Texas A&M High Performance Research Computing, and the Department of Electrical & Computer Engineering, Dr. Jian Tao at COE-HPC will teach a special topic course – ECEN 489 section 504 (CRN 40958) to undergraduate students on various subjects in Data Science in Fall 2019.
The course introduces students to the computational practice of Data Science through a sequence of interactive modules that provide an integrated hands-on approach to its methods, tools, and applications, and supporting technologies including high performance and cloud computing platforms. These modules prepare students for a concurrent semester-long project involving real-world applications of Data Science. The course is aimed both at students who wish to acquire knowledge of Data Science by developing fluency in its applications, and also students with previous exposure to Data Science foundations who wish to develop complementary skills in the use of state of the art systems and tools.
More about the course can be found here.
Description
The NVIDIA Deep Learning Institute (DLI), the Texas A&M Institute of Data Science, the Texas A&M High Performance Research Computing, and the Texas Engineering Experiment Station invite you to attend a hands-on deep learning workshop on September 7th, 2019 from 8:30AM to 5:00PM at the ILSB Auditorium exclusively for verifiable academic students, staff, and researchers. NVIDIA DLI offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning and accelerated computing.
Learning Objectives
At the conclusion of the workshop, you’ll have an understanding of the fundamentals of deep learning and be able to:
Implement common deep learning workflows, such as image classification and object detection
Experiment with data, training parameters, network structure, and other strategies to increase performance and capability of neural networks
Integrate and deploy neural networks in your own applications to start solving sophisticated real-world problems
Upon completion of this Lab, you will be able to implement deep learning to solve problems in the real world.
Workshop Outline
08:30 — Registration (refreshments & soft drinks)
09:00 — Introduction
Meet the instructor.
Create an account at courses.nvidia.com/join
09:15 — Unlocking New Capabilities
Learn the biological inspiration behind deep neural networks (DNNs).
Explore training DNNs with big data.
Train neural networks to perform image classification by harnessing the three main ingredients of deep learning: deep neural networks, big data, and the GPU.
10:00 — Break (refreshments & soft drinks)
10:15 — Unlocking New Capabilities and Measuring and Improving Performance
Deploy trained neural networks from their training environment into real applications.
Optimize DNN performance.
Incorporate object detection into your DNNs.
12:00 — Lunch (provided)
13:00 — Final Project
Validate learnings by applying the deep learning application development workflow (load dataset, train, and deploy model) to a new problem.
Learn how to set up your GPU-enabled environment to begin work on your own projects.
Explore additional project ideas and resources to get started with NVIDIA AMI in the cloud, nvidia-docker, and the NVIDIA DIGITS container.
15:00 — Break (refreshments & soft drinks)
15:15 — Final Review
Review key learnings and wrap up questions.
Complete the assessment to earn a certificate.
Take the workshop survey.
16:45 — Wrap-up
Prerequisites:
Familiarity with the basic programming, fundamentals such as functions and variables.
NVIDIA DLI Certification:
Through built-in assessments, participants can earn certification to prove subject matter competency and support professional career growth.
Workshop Setup Instructions:
1. Create an NVIDIA Developer account at http://courses.nvidia.com/join.
2. Make sure that WebSockets works for you:
Test your laptop at http://websocketstest.com
Under ENVIRONMENT, confirm that “WebSockets” is checked yes.
Under WEBSOCKETS (PORT 80), confirm that “Data Receive”, “Data Send”, and “Echo Test” are checked yes.
3. If there are issues with WebSockets, try updating your browser. We recommend Chrome, Firefox, or Safari for an optimal performance.
4. Once onsite, visit http://courses.nvidia.com/dli-event and enter the event code provided by the instructor.
Parking:
If you have a valid A&M parking permit, you can park in the “night and weekend authorized” lots.
http://transportmap.tamu.edu/parkingmap/tsmap.htm?map=naw