Multi-threaded software development is a huge challenge facing software developers. The model we have taken enables massive amounts of computation to be distributed across all available cores.
The combination of task based parallelism, and data based parallelism, orchestrated using a dependency graph, enable our scheduler to very efficiently manage the CPU(and in the GPU in the future).
This model is used in high end video game engines today to leverage multi-core CPUs effectively. We make this programming model available in Python and JavaScript.
When we designed Fabric, our goal was not to speed up Python, or JavaScript. Our goal was to build high performance multi-threaded applications on top or dynamic languages.
Fabric is for software developers who need to build high performance software, and also use dynamic languages.
V8 will continue to speed up, and may even get close to the speed of native code. But in that time, CPUs architectures will continue to gain more cores, widening the gap between multi-threaded code, and dynamic code.
The combination of task based parallelism, and data based parallelism, orchestrated using a dependency graph, enable our scheduler to very efficiently manage the CPU(and in the GPU in the future).
This model is used in high end video game engines today to leverage multi-core CPUs effectively. We make this programming model available in Python and JavaScript.
http://s09.idav.ucdavis.edu/talks/04-JAndersson-ParallelFros...
A single call from Python/JavaScript can kick off hundreds of tasks (written in KL) to be scheduled and executed.