Fifth Computational and Data Science school for HEP (CoDaS-HEP 2023)

17 Jul 2023, 08:30 → 21 Jul 2023, 13:00 US/Eastern

407 Jadwin Hall (Princeton University)

The fifth school on tools, techniques and methods for Computational and Data Science for High Energy Physics (CoDaS-HEP 2023) will take place on 17-21 July, 2023, at Princeton University.

Advanced software is a critical ingredient to scientific research. Training young researchers in the latest tools and techniques is an essential part of developing the skills required for a successful career both in research and in industry.

The CoDaS-HEP school aims to provide a broad introduction to these critical skills as well as an overview of applications High Energy Physics. Specific topics to be covered at the school include:

• Parallel Programming 
• Big Data Tools and Techniques
• Machine Learning 
• Practical skills like performance evaluation, collaborative use of git/github, etc.

The school offers a limited number of young researchers an opportunity to learn these skills from experienced scientists and instructors. Successful applicants will receive travel and lodging support to attend the school.

School website: http://codas-hep.org   

The school lectures will take place in 407 Jadwin Hall, in the main lecture hall of the Princeton Center for Theoretical Science (PCTS).

This event is supported by National Science Foundation grants OAC-1829707, OAC-1829729, OAC-2017699 and OAC-1836650, the Princeton Institute for Computational Science and Engineering (PICSciE), the Princeton Physics Department, the Office of the Dean for Research of Princeton University and the Enrico Fermi Institute at the University of Chicago. Any opinions, findings, conclusions or recommendations expressed in this material are those of the developers and do not necessarily reflect the views of the National Science Foundation.

 

Monday 17 July

08:30 Breakfast

1 Welcome and Overview

Speaker: Peter Elmer (Princeton University (US))

2 Collaborative Software Development with Git(Hub)

Git is perhaps the single biggest denominator among all software developers, regardless of field or programming language. It serves not only as a version control system but as the backbone of all collaborative software development.

This session aims to be 100% hands-on and at least 90% collaborative. We will exclusively work in the browser, using GitHub and GitHub codespaces. Learn forking, branching, opening pull requests, handling merge requests, and more. GitHub account required.

Speaker: Kilian Lieret (Princeton University)

0 Slides (preferred)

1 Playground repo

2 Slides (fallback)

3 Contribute to slides

10:30 Coffee Break

3 What Every Computational Physicist Should Know About Computer Architecture

These days, everyone in physics in a computational physicist in one way or another. Experiments, theory, and (obviously) simulations all rely heavily on computers. Isn't it time you got to know them better? Computer architecture is an interesting study in its own right, and how well one understands and uses the capabilities of today's systems can have real implications for how fast your computational work gets done. Let's dig in, learn some terminology, and find out what's in there.

Speaker: Steven R Lantz (Cornell University (US))

WhatEveryCompPhysShouldKnowArch.pdf

4 Vector Parallelism on Multi-Core Processors

All modern CPUs boost their performance through vector processing units (VPUs). VPUs are activated through special SIMD instructions that load multiple numbers into extra-wide registers and operate on them simultaneously. Intel's latest processors feature a plethora of 512-bit vector registers, as well as 1 or 2 VPUs per core, each of which can operate on 16 floats or 8 doubles in every cycle. Typically these SIMD gains are achieved not by the programmer directly, but by (a) the compiler through automatic vectorization of simple loops in the source code, or (b) function calls to highly vectorized performance libraries. Either way, vectorization is a significant component of parallel performance on CPUs, and to maximize performance, it is important to consider how well one's code is vectorized. We will take a look at vector hardware, then turn to simple code examples that illustrate how compiler-generated vectorization works.

Speaker: Steven R Lantz (Cornell University (US))

VectorParallelismMultiCoreProcs.pdf

12:30 Lunch

5 Parallel Programming - An introduction to parallel computing with OpenMP

We start with a discussion of the historical roots of parallel computing and how they appear in a modern context. We'll then use OpenMP and a series of hands-on exercises to explore the fundamental concepts behind parallel programming.

Speaker: Tim Mattson (Intel)

ParProgForPhys GitHub repo

15:00 Coffee Break

6 Parallel Programming - The OpenMP Common Core

We will explore through hands-on exercises the common core of OpenMP; that is, the features of the API that most OpenMP programmers use in all their parallel programs. This will provide a foundation of understanding you can build on as you explore the more advanced features of OpenMP.

Speaker: Tim Mattson (Intel)

18:00 Welcome Light Reception

Tuesday 18 July

08:00 Breakfast

7 Parallel Programming - Working with OpenMP

We now know how to work with threads directly and how to parallelize loops with OpenMP directives. Now we move on managing the data environment. Our Hands-on exercises will be much more complicated as we explore how to debug multithreaded programs. Then we move on to task-level parallelism in OpenMP and wrap up with a look at the core design patterns of OpenMP.

Speaker: Tim Mattson (Intel)

8 Group Photo - Jadwin Hall plaza

10:40 Coffee Break

9 Parallel Programming - The world beyond OpenMP

Parallel programming is hard. There is no way to avoid that reality. We can mitigate these difficulties by focusing on the fundamental design patterns from which most parallel algorithms are constructed. Once mastered, these patterns make it much easier to understand how your problems map onto other parallel programming models. Hence for our last session on parallel programming, we'll review these essential design patterns as seen in OpenMP, and then show how they appear in cluster computing (with MPI) and GPGPU computing (with OpenMP and then a quick survey of other GPGPU languages).

Speaker: Tim Mattson (Intel)

12:30 Lunch

10 The Scientific Python Ecosystem

In recent years, Python has become a glue language for scientific computing. Although code written in Python is generally slow, it has a good connection with compiled C code and a common data abstraction through Numpy. Many data processing, statistical, and most machine learning software has a Python interface as a matter of course.

This tutorial will introduce you to core Python packages for science, such as NumPy, SciPy, Matplotlib, Pandas, and Numba, (part 1) as well as HEP-specific tools like iminuit, particle, pyjet, and pyhf (part 2). We'll especially focus on accessing ROOT data in uproot and awkward. Part 1 will also cover the Scientific Python Development Guide and a short discussion on packaging.

Speaker: Henry Fredrick Schreiner (Princeton University)

ISciNumPy

Level Up Your Python

Packaging tutorial

Python Performance Minicourse

Scientific Python Development Guide

15:00 Coffee Break

11 The Scientific Python Ecosystem

Continued from last time. Part 2 focuses on the HEP portion of the ecosystem.

Speaker: Henry Fredrick Schreiner (Princeton University)

HSF Training: Scikit-HEP

Scikit-HEP

18:30 BBQ and Drinks - Palmer House

Wednesday 19 July

08:00 Breakfast

12 Floating Point Arithmetic Is Not Real

Speaker: Ianna Osborne (Princeton University)

Exercise notebook

Floating-point Arithmetic is not Real.pdf

13 The Use and Abuse of Random Numbers

Speaker: David Lange (Princeton University (US))

Exercise repo

Slides

10:30 Coffee Break

14 Vector Parallelism on Multi-Core Processors (continued)

Speaker: Steven R Lantz (Cornell University (US))

15 Introduction to Performance Tuning & Optimization Tools

Improving the performance of scientific code is something that is often considered to be an art that is difficult, mysterious, and time-consuming, but it doesn't have to be. Performance tuning and optimization tools can greatly aid in the evaluation and understanding of the performance of scientific code. In this talk we will discuss how to approach performance tuning and introduce some measurement tools to evaluate the performance of compiled-language (C/C++/Fortran) code. Powerful profiling tools, such as Intel VTune and Advisor, will be introduced and discussed.

Speaker: Steven R Lantz (Cornell University (US))

IntroPerfTuningOpt.pdf

16 Performance Case Study: the mkFit Particle Tracking Code

This is a demo of how to run various analyses with Intel Advisor, to see what they reveal about hotspots in current version of the mkFit particle tracking code; these may represent opportunities for improving the code's performance.

Speaker: Steven R Lantz (Cornell University (US))

12:30 Lunch

17 Machine Learning: Introduction to Machine Learning

Speakers: Abhijith Gandrakota (Fermi National Accelerator Lab. (US)), Adrian Alan Pol (Princeton University (US))

Exercises

15:00 Coffee Break

18 Machine Learning: Supervised Deep Learning

Speakers: Abhijith Gandrakota (Fermi National Accelerator Lab. (US)), Adrian Alan Pol (Princeton University (US))

MLp2_Abhijith_Gandrakota.pdf

Setup.pdf

Supervised Deep Learning: Basics of deep learning, backpropagation, optimization i.e. Multi-Layer Perceptrons (MLPs)

18:00 Dinner on your own

Thursday 20 July

08:00 Breakfast

19 Machine Learning: Convolutional Neural Networks and Autoencoders

Speakers: Abhijith Gandrakota (Fermi National Accelerator Lab. (US)), Adrian Alan Pol (Princeton University (US))

10:00 Coffee Break

20 Machine Learning: Permutation Invarience

Speakers: Abhijith Gandrakota (Fermi National Accelerator Lab. (US)), Adrian Alan Pol (Princeton University (US))

MLp4_Abhijith_Gandrakota.pdf

12:30 Lunch

21 Columnar Data Analysis

Data analysis languages, such as Numpy, MATLAB, R, IDL, and ADL, are typically interactive with an array-at-a-time interface. Instead of performing an entire analysis in a single loop, each step in the calculation is a separate pass, letting the user inspect distributions each step of the way.

Unfortunately, these languages are limited to primitive data types: mostly numbers and booleans. Variable-length and nested data structures, such as different numbers of particles per event, don't fit this model. Fortunately, the model can be extended.

This tutorial will introduce awkward-array, the concepts of columnar data structures, and how to use them in data analysis, such as computing combinatorics (quantities depending on combinations of particles) without any for loops.

Speakers: Ioana Ifrim (Princeton University (US)), Jim Pivarski (Princeton University)

Columnar Data Analysis Materials

15:00 Coffee Break

22 Columnar Data Analysis

Speakers: Ioana Ifrim (Princeton University (US)), Jim Pivarski (Princeton University)

Columnar Data Analysis Materials

18:00 School Dinner - Frick Atrium and Patio

Friday 21 July

08:30 Breakfast

23 Things you didn't know you needed

Speakers: Henry Fredrick Schreiner (Princeton University), Kilian Lieret (Princeton University)

Repo

Slides

slides.pdf

24 Parallelized Track Reconstruction for the LHC: the mkFit Project

In this presentation, we consider how a physics application may be restructured to take better advantage of vectorization and multithreading. For vectorization, we focus on the Matriplex concept that is used to implement parallel Kalman filtering in our collaboration's particle tracking R&D project called mkFit. Drastic changes to data structures and loops were required to help the compiler find the SIMD opportunities in the algorithm. For multithreading, we examine how binning detector hits and tracks in an abstraction of the detector geometry enabled track candidates to be processed in bunches. We conclude by looking at how Intel VTune and Advisor, together with simple test codes, played a role in identifying and resolving trouble spots that affected performance. The mkFit code is now part of the production software for CMS in LHC Run 3.

Speaker: Steven R Lantz (Cornell University (US))

ParallelizedTrackReco.pdf

10:30 Coffee Break

25 Closing Session