InfoQ Homepage GPU Content on InfoQ
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OpenAI Releases Triton, Python-Based Programming Language for AI Workload Optimization
OpenAI released their newest language, Triton, an open-source programming language that enables researchers to write highly efficient GPU code for AI workloads. Triton is Python-compatible and allows new users to achieve expert-quality results in only 25 lines of code. The code is written in Python using Triton’s libraries, which are then JIT-compiled to run on the GPU.
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Microsoft Announces the General Availability of Azure ND A100 V4 Cloud GPU Instances
Recently Microsoft announced the general availability of the Azure ND A100 v4 Cloud GPU instances—powered by NVIDIA A100 Tensor Core GPUs. These Virtual Machines (VMs) are targeted at customers with high performance and demanding workloads like Artificial Intelligence (AI) and Machine Learning (ML) workloads.
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Deno 1.8 Ships with WebGPU Support, Dynamic Permissions, and More
Deno 1.8 recently shipped with plenty of new features, including WebGPU support, internationalization APIs, stabilized import maps, support for fetching private modules, and more. Deno permissions, links, and symlinks are now stable. Deno 1.8 additionally ships with TypeScript 4.2.
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Is Julia Production Ready? Q&A with Bogumił Kamiński
On the heels of JuliaCon 2020, SGH Warsaw School of Economics professor and DataFrames.jl maintainer Bogumił Kamiński summarized the status of the language and its ecosystem and stated that Julia is finally production-ready. InfoQ has taken the chance to speak with professor Kamiński.
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Concurnas: the New Language on the JVM for Concurrent and GPU Computing
Concurnas is a new open source JVM programming language designed for building concurrent and distributed systems. Concurnas is a statically typed language with object oriented, functional, and reactive programming constructs. With native support for GPU computing and vectorization, Concurnas allows for building machine learning applications and high performance parallel applications.
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TornadoVM: Running Java on GPUs and FPGAs with Dr Juan Fumero at QCon London
Dr Juan Fumero presented at QCon London on TornadoVM, a plug-in to OpenJDK and GraalVM that runs Java on heterogeneous hardware including Graphical Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs). Demos during the presentation showed code being sped up by hundreds of times when running on a GPU vs a CPU.
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Boosting Apache Spark with GPUs and the RAPIDS Library
At the 2019 Spark AI Summit Europe conference, NVIDIA software engineers Thomas Graves and Miguel Martinez hosted a session on Accelerating Apache Spark by Several Orders of Magnitude with GPUs and RAPIDS Library. InfoQ recently talked with Jim Scott, head of developer relations at NVIDIA, to learn more about accelerating Apache Spark with GPUs and the RAPIDS library.
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PyTorch 1.1 Release Improves Performance, Adds New APIs and Tools
Facebook AI Research announced the release of PyTorch 1.1. The latest version of the open-source deep learning framework includes improved performance via distributed training, new APIs, and new visualization tools including native support for TensorBoard.
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Uber Introduces AresDB: GPU-Powered, Open-Source, Real-Time Analytics Engine
Uber recently introduced AresDB, an open-source real-time analytics engine leveraging an unconventional power source - graphics processing units (GPUs) - for meeting the growing demands of analysis at scale and at the same time unifying, simplifying and improving Uber’s existing solutions.
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GPUs Found Vulnerable to Side-Channel Attacks
Since Spectre and Meltdown were demonstrated at the beginning of 2018, researchers have been discovering many variants of side-channel vulnerabilities affecting both Intel and AMD CPUs. GPUs seemed instead to be immune to such attacks. Until now, that is.
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Microsoft Pushes New Azure Offerings into the High-Performance Computing Market
Microsoft is entering the high-performance computing (HPC) market with their announcement of the general availability of Azure CycleCloud, a tool for creating, managing, operating, and optimizing HPC clusters of any scale in Azure. Furthermore, Microsoft announced it would support NVIDIA GPU Cloud (NGC).
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GPUs on Google's Kubernetes Engine Are Now Generally Available
Google announced the general availability of GPUs in their Kubernetes Engine (GKE). Together with the recent GA of 1.10 version of GKE customers can land their machine learning (ML) workloads on to it and leverage the massive processing power of the GPUs.
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Google Introduces Low-Priced Preemptible GPUs for Their Customers
Google announces the beta release of Graphical Processing Units (GPUs) attached to Preemptible Virtual Machines (VMs) in their cloud Platform. Google Cloud Platform (GCP) customers can now attach NVIDIA K80 and NVIDIA P100 GPUs to Preemptible VMs for respectively 0.22 and 0.73 dollar cent per GPU hour, 50 percent cheaper than GPUs connected to on-demand instances.
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Building GPU Accelerated Workflows with TensorFlow and Kubernetes
Daniel Whitenack spoke at the recent KubeCon + CloudNativeCon North America 2017 Conference about GPU based deep learning workflows using TensorFlow and Kubernetes technologies. He discussed the open source data pipeline framework Pachyderm.
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Using C# to Target GPUs
The new Hybridizer technology provides C# developers with a way to target the CUDA platform and take advantage of GPUs for increased performance. Thanks to Hybridizer, developers are not forced to use C or C++ to write high-performance GPU code.