A very exciting development indeed has come to my attention albeit four days late. This news is, without exaggeration, in my humble opinion nothing short of groundbreaking, not only because it pushes the boundaries and capabilities of Java even further in terms of hardware support and performance but also because, as I’m gradually beginning to realise, the future of high performance computing is in the GPU.
This project intends to enable Java applications to seamlessly take advantage of a GPU–whether it is a discrete device or integrated with a CPU–with the objective to improve the application performance.
Their focus will be on code generation, garbage collection and runtimes.
We propose to use the Hotspot JVM, and will concentrate on code generation, garbage collection, and
runtimes. Performance will be improved, while preserving compile time, memory consumption and code generation quality.
The project will also use the new Java 8 lambda language and may eventually sprout further platform enhancements.
We will start exploring leveraging the new Java 8 Lambda language and library features. As this project progress, we may identify challenges with the Java API and constructs which may lead to new language, JVM and library extensions that will need standardization under the JCP process.
John Coomes (Oracle) will lead the project and Gary Frost (AMD) has gladly offered resources from AMD. As the mail thread got underway two already existing related projects were brought to the list’s attention: rootbeer (PDF, slashdot) and Aparapi (AMD page). Rootbeer is a particularly interesting project as it performs static analysis of Java code and generates CUDA code automatically – quite different from compile time OpenCL bindings from Java. The developer of rootbeer has also shown interest in joining the openjdk project. John Rose, Oracle HotSpot developer, also posted a talk he recently gave on Arrays 2.0 which I’m yet to watch.
The missing link in deriving value from GPUs, from what I understand in a domain that I’m still fairly new to, is that GPU hardware and programming need to be made accessible to the mainstream and that’s the purpose I hope this project will serve. I hope also that, once underway, it also raises wider awareness of how we must make a mental shift from concurrency to parallelism and from data parallelism to task parallelism and how the next generational leap in parallelism will come, not from cpu cores or concurrency frameworks, but from harnessing the mighty GPU.
Hacker news has some discussion. What do you think? Let me know.