by phuongdzu on 25 March 2016 - 1 003 views - 0 comments

Fabric Engine 2.1

Fabric Engine 2.1

Duration Project Files Included MP4

Info:
What’s new in Fabric Engine 2.1?
February 26, 2016

Fabric Engine 2.1 is the first big update since we shipped 2.0 last September. Fabric 2 introduced the Canvas visual programming system and we’ve been paying close attention to what you thought of it. It was clear from the feedback that we needed to make your Fabric Engine experience – from getting started all the way to building complex tools – more intuitive and more productive. So we concentrated on making it much easier for you to get started with Canvas by providing more presets, more samples and better visual debugging info. And don’t worry, there are plenty of goodies for people focused on building more complex tools.
Multithreaded. GPU Accelerated
Multi-core CPUs are common. Programmers skilled in writing code that takes full advantage of them are not. Rarer still are those who can leverage the power of GPUs. At the core of Fabric is an engine that does both.
Build Better Tools Faster
Artists and programmers can use Fabric Canvas, a visual, node and connection interface to create their own tools. Experimentation is fast and fluid with immediate feedback. Not only is creating and modifying a tool quick, but the tool itself is really fast – each node in Fabric Canvas is expressed in KL “under the hood”, and runs at speeds comparable with compiled C++. You can take control of streamlining your workflow.
Leave the Heavy Lifting to Fabric Engine
Programmers will appreciate the heavy lifting done by the Fabric Core, which provides easy-to-use, high-level primitives for task- and data-based parallelism. Instrumentation methods start and stop collection of data to help you profile time spent in user code (KL operators), the Fabric Core or external to Fabric.
Leverage ALL the Power
You don’t need to master CUDA or OpenCL to take advantage of the massive performance potential available with modern GPUs. KL code runs without modification on CUDA GPUs. This means that many more tools can be GPU accelerated, as the cost and risk of doing so is near zero. The tool is simply run on either the CPU or the GPU, depending on which offers the highest performance for a particular tool.

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