GPU Accelerated Computing and Optimizations on Cross-Vendor Graphics Cards with Vulkan & Kompute
Many advanced data processing paradigms fit incredibly well to the parallel-architecture that GPU computing offers, and exciting advancements in the open source projects such as the Vulkan SDK (a collection of tools for cross-vendor GPU development) and the Kompute Project (a Linux Foundation open source project that enables cross-vendor general-purpose GPU programming) are enabling developers to take advantage of general purpose GPU computing capabilities in cross-vendor mobile and desktop GPUs, including over 1000 GPUs across AMD, Qualcomm, NVIDIA & many more graphics processors. In this talk we will provide a conceptual and practical insight into the cross-vendor GPU compute ecosystem as well as how to adopt these tools to add GPU acceleration to your existing C++ applications.
In this talk we will show how you can write a simple GPU accelerated machine learning algorithm from scratch which will be able to run on virtually any GPU. We will give an overview on the projects that are making it possible to accelerate applications across cross-vendor GPUs. We'll show how you can get started with the full power of your GPU using the Kompute framework with only a handful of lines of C++ code, as well as providing an intuition around how optimizations can be introduced through the lower level C++ interface.
As part of the more advanced example, we will showcase some optimizatiosn that can be leveraged through the hardware capabilities of relevant graphics cards, such as concurrency-enabled GPU queues which allow us to introduce 2x+ performance improvements into advanced data processing workloads. We will dive into the GPU computing terminology around asynchronous & parallel workflow processing, cover the core principles of 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 more recent GPU architectures which will support 3 or now even more parallel queue processing workloads).
Alejandro Saucedo
Alejandro is the Chief Scientist at the Institute for Ethical AI & Machine Learning, where he contributes to policy and industry standards on the responsible design, development and operation of AI, including the fields of explainability, GPU acceleration, ML security and other key machine learning research areas. Alejandro Saucedo is Director of Engineering at Seldon Technologies, where he leads teams of machine learning engineers focused on the scalability and extensibility of machine learning deployment and monitoring products. With over 10 years of software development experience, Alejandro has held technical leadership positions across hyper-growth scale-ups and has a strong track record building cross-functional teams of software engineers. He is currently appointed as governing council Member-at-Large at the Association for Computing Machinery, and is currently the Chairperson of the GPU Acceleration Kompute Committee at the Linux Foundation.
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