07-09, 09:00–12:00 (Europe/Amsterdam), TU-Eindhoven -1.350
Everybody wants fast code, but not everyone knows how to write it. Thankfully, Dagger.jl can help you write fast, parallel code with such ease as you've never known before. In this workshop, we will teach you everything you need to know to use Dagger effectively, and work through building example parallel programs with Dagger.
Traditional parallel programming can be very hard to do effectively, as there are many prerequisites to know, and many tools that need to be used correctly and combined for maximum performance. Because of this, many programmers never learn how to write code that can scale beyond a single thread or a single server; and when parallelism is added to code, it often only supports one or a limited number of systems and hardware devices, which excludes users with unsupported systems from using that code effectively.
Dagger.jl aims to help fix this problem for Julia code, by providing a full parallel programming environment that closely matches how users already write code. Instead of forcing users to learn a new programming paradigm or opinionated API, Dagger provides a variety of familiar interfaces - like tasks, arrays, tables, and graphs - so that users can pick the interface that matches their existing code and algorithms. And once any of those interfaces are used, Dagger seamlessly parallelizes the user's code and allows it to run automatically on multiple CPUs, multiple servers, on a variety of GPUs, and more, and even manages data by moving it to disk when available memory is tight.
This workshop is focused on users who feel comfortable with Julia, but struggle with adding parallelism to their code. This workshop will start out by introducing Dagger and explaining how it works at a high level. Then, we will dive into the various programming interfaces (tasks, arrays, tables, graphs, and more) that Dagger provides, show how they can be used just like their non-Dagger counterparts, and then work through an example program built with each programming paradigm. Along the way, we will also see how to user Dagger's logging and profiling capabilities to gain insights into what Dagger is doing behind the scenes, so that we can better observe where our parallelism is coming from.
If there is enough time, we will also cover more advanced topics like how to utilize GPUs, MPI interfacing, and more.
Przemysław Szufel is an Assistant Professor at SGH Warsaw School of Economics, Adjunct Professor at Toronto Metropolitan University. His main research focus is applying advanced analytics methods, and in particular, machine learning, simulation and optimization in modelling in bringing new value to business processes. He is a co-author of several tools and algorithms for optimal and cost efficient collection and analysis of large data sets in the cloud. He is a co-author of over 40 publications, including handbooks and journal papers, in the area of applying advanced analytics, machine learning and simulation methods to making optimal business decisions. He is an active member of the Julia language community - maintains 4 official Julia packages and has 2nd place answering Julia-related questions on StackOverflow. He is a co-author of book “Julia 1.0 Programming Cookbook: Over 100 numerical and distributed computing recipes for your daily data science workflow”. Przemysław is also co-managing SilverDecisions.pl project (that aims for representing and supporting business decisions), which has been elected by the European Commission to the Innovation Radar programe, grouping the best innovations financed by the EU funds.
I'm a Research Software Engineer at MIT's JuliaLab, working on parallel programming with Dagger.jl and AMDGPU.jl. I love working on low-level runtimes and compilers, as well as building out high-level, user-friendly parallel programming interfaces.