HPC, Big Data and Data Science

GPU Computing Made Simple with the C++ Vulkan SDK & the C++ Kompute Framework (AMD, Qualcomm, NVIDIA & Friends)

D.hpc
Alejandro Saucedo
Many advanced data processing paradigms fit incredibly well to the parallel-architecture that GPU computing offers, which has resulted in the continuously growing adoption of graphics card for general purpose computing. Exciting advancements in the open source Vulkan Project are enabling developers to take advantage of general purpose GPU computing capabilities in cross-vendor mobile and desktop GPUs including AMD, Qualcomm, NVIDIA & friends. In this talk we will learn to write GPU accelerated algorithms which will be able to run on virtually any GPU hardware, including non-NVIDIA GPUs. We'll introduce an intuition and key concepts around GPU computing, as well as show how you can get started with the Vulkan Kompute framework with only a handful of lines of C++ or Python code. Throughout the talk we will also dive into the GPU computing terminology around asynchronous & parallel workflow processing, cover the core principles of machine learning data parallelism, explain the hardware concepts of GPU queues & queueFamilies, and talk about how advancements in new and upcoming graphics cards will enable for even bigger speedups (such as the NVIDIA Ampere GA10x architecture which will support up to 3 parallel queue processing workloads). In more detail these are the topics of the talk: • GPU computing intuition, hardware and foundations • Deeper dive into the OSS Vulkan C++ SDK enabling cross-vendor GPU computing • The C++ Kompute Framework and its architecture which augments Vulkan • Simple C++ Example with Kompute • Deeper Optimizations (Batch Commands, Asynchronous and Parallel Workloads) • FamilyQueues for Hardware-Parallel Workloads • C++ Example for FamilyQueue Hardware-Parallel Workload A more in-depth version of this talk can be found in this blog post: • https://towardsdatascience.com/parallelizing-heavy-gpu-workloads-via-multi-queue-operations-50a38b15a1dc If you are interested in the higher level use-cases, as well as machine learning examples, you can join the talk at the Python Room: • https://fosdem.org/2021/schedule/event/python_cuda/ Other useful links: • Vulkan Kompute Repo: https://github.com/EthicalML/vulkan-kompute • Vulkan Kompute Docs: https://kompute.cc/

Additional information

Type devroom

More sessions

2/6/21
HPC, Big Data and Data Science
Ali Hajiabadi
D.hpc
With the end of Moore’s law, improving single-core processor performance can be extremely difficult to do in an energy-efficient manner. One alternative is to rethink conventional processor design methodologies and propose innovative ideas to unlock additional performance and efficiency. In an attempt to overcome these difficulties, we propose a compiler-informed non-speculative out-of-order commit processor, that attacks the limitations of in-order commit in current out-of-order cores to ...
2/6/21
HPC, Big Data and Data Science
Christian Kniep
D.hpc
The Container ecosystem spans from spawning a process into an isolated and constrained region of the kernel at bottom layer, building and distributing images just above to discussions on how to schedule a fleet of containers around the world at the very top. While the top layers get all the attention and buzz, this session will base-line the audiences' understanding of how to execute containers.
2/6/21
HPC, Big Data and Data Science
Nicolas Poggi
D.hpc
Over the years, there has been extensive efforts to improve Apache Spark SQL performance. This talk will introduce the new Adaptive Query Execution (AQE) framework and how it can automatically improve user query performance. AQE leverages query runtime statistics to dynamically guide Spark's execution as queries run along. The talk will go over the main features in AQE and provide examples on how it can improve on the previous static query plans. Finally, we'll present the significant ...
2/6/21
HPC, Big Data and Data Science
Mohammad Norouzi
D.hpc
This talk introduces DiscoPoP, a tool which identifies parallelization opportunities in sequential programs and suggests programmers how to parallelize them using OpenMP. The tool first identifies computational units which, in our terminology, are the atoms of parallelization. Then, it profiles memory accesses inside the source code to detect data dependencies. Mapping dependencies to CUs, we create a data structure which we call the program execution tree (PET). Further, DiscoPoP inspects the ...
2/6/21
HPC, Big Data and Data Science
Alaina Edwards
D.hpc
In this talk we explore two programming models for GPU accelerated computing in a Fortran application: OpenMP with target directives and CUDA. We use an example application Riemann problem, a common problem in fluid dynamics, as our testing ground. This example application is implemented in GenASiS, a code being developed for astrophysics simulations. While OpenMP and CUDA are supported on the Summit supercomputer, its successor, an exascale supercomputer Frontier, will support OpenMP and ...
2/6/21
HPC, Big Data and Data Science
Bob Dröge
D.hpc
The European Environment for Scientific Software Installations (EESSI, pronounced as “easy”) is a collaboration between different HPC sites and industry partners, with the common goal to set up a shared repository of scientific software installations that can be used on a variety of systems, regardless of which flavor/version of Linux distribution or processor architecture is used, or whether it is a full-size HPC cluster, a cloud environment or a personal workstation. The EESSI codebase ...
2/6/21
HPC, Big Data and Data Science
Robert McLay
D.hpc
XALT is a tool run on clusters to find out what programs and libraries are run. XALT uses the environment variable LD_PRELOAD to attach a shared library to execute code before and after main(). This means that the XALT shared library is a developer on every program run under linux. This shared library is part of every program run. This talk will discuss the various lessons about routine names and memory usage. Adding XALT to track container usage presents new issues because of what shared ...