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From Shubham Mehta <shubham.meht...@gmail.com>
Subject Java On GPU: Rootbear, Java 8
Date Tue, 27 Oct 2015 16:57:02 GMT

As you know Edward suggested that we can go by Rootbear(
So, I was going through their paper regarding how they supported GPU
acceleration for Java.

The main idea is cross compilation of Java bytecode to CUDA. They have
tried to get highest performance in (de)serialization to and from GPU
memory by comparing few approaches: JNI, Pure Java and Reflection. Finally,
they used Pure Java to read everything into Java byte array. After that,
one JNI call to copy everything to GPU memory.

Each Java Object is represented in GPU memory as two segments-Static mem.
and Instance mem. They mostly haven't done Garbage Collection on GPU, which
I think shouldn't bother us.

For code conversion to CUDA, they use *Soot Optimization Framework* (Raja
Vallée-Rai, Laurie Hendren, Vijay Sundaresan, Patrick Lam, Etienne Gagnon
and Phong Co. Soot - a Java Optimization Framework) to load .class files
from jar to an intermediate in memory representation called Jimple which is
then analyzed to generate CUDA code.

*In short, it will server our purpose to run computation support GPUs for
neuron-centric programming model.*

But before taking final decision, it would be great if someone can look
into the following paper:
"*Evaluation of Java for General Purpose GPU Computing "*
This paper cites Rootbear and has done comparative study of all available
framework for running Java code on GPGPU. I couldn't access it.

Also, I read that Java 8 provides parallel stream APIs with lambda
expressions to facilitate parallel programming for multi-core CPUs and
many-code GPUs. Does anyone know about this in more detail?

Lastly, people are trying to use ML to decide between GPU and CPU during
runtime. I don't know how well it scales. It is very recent study.
"*Machine-Learning-based Performance Heuristics for Runtime CPU/GPU

Best Regards,

Shubham Mehta
B.Tech 2015
Computer Science and Engineering
IIT Bombay

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