A little shocking discovery we had on the Java ThreadPoolExecutor implementation: our large training cluster would somehow never filled up with a specific kind of training task. The culprit was eventually identified in the ThreadPoolExecutor, which gracefully queued up all tasks. I always thought new threads will be created when the queue is not empty and the number of threads is less than the maxPoolSize. Obviously, I did not read the fine-print in JavaDoc. There were quite a few different strategies described in ThreadPoolExecutor: Direct Handoffs (never queue, always create new thread till reach max), Unbounded queues (always queue, and never reach max threads) and Bounded queues (queue to a certain size, then start ceating threads till max threads, then rejects).
My assumption, Direct Handoffs+Unbounded Queue (use as many threads till reach maxPoolSize, then queue up unbounded), was obviously not one of them, even though I would think this is definitely favourable strategy in most cases. Anyway, we implemented this using a customized LinkedBlockingQueue.
Lessons learned: (like dealing with the bank,) always read the fine-print;).
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ReplyDeleteI know, I know, I know...
ReplyDeleteDo your guys use a pure java trainer for AMs or make system calls to binaries? That may make a quite big difference on the design of the parallelization platform. However I wonder if there exists toolkits for AM training in pure java...
ReplyDeleteWorks for me with such customized queue:
ReplyDeletestatic class ThreadPoolTaskQueue extends LinkedBlockingQueue{
private ThreadPoolExecutor threadPool = null;
@Override
public boolean offer(Runnable e) {
if(threadPool != null && threadPool.getPoolSize() < threadPool.getMaximumPoolSize())
return false;
else
return super.offer(e);
}
public void setThreadPool(ThreadPoolExecutor threadPool){
this.threadPool = threadPool;
}
}
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Java Thread Pools are quite a useful utility for handling concurrent tasks particularly when dealing with large training procedures. Thus, saving up on thread utilization, they cut down on overhead and increase speed on your academic work, could benefit from cheap dissertation writing to manage your deadlines effectively
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