New Prediction Paradigms for Parallel and Distributed Systems

本研究旨在扩展和重新定义基于预测的技术在并行和分布式系统中的作用。首先，我们通过在屏障之前预测值的最终生产者来减少屏障同步开销。这种生产者标识允许消费者推测地越过障碍，只在需要时等待实际生产。其次，我们将滑流范式引入多处理器系统。每个并行线程的冗余版本并发运行，通过推测性地删除长延迟事件（如共享内存写）来减少其执行。减少的线程动态检测共享模式，原始线程使用共享模式来优化其一致性和同步操作，从而提高整体性能。最后，我们研究了使用生产者验证的消息预测来减少消息传递环境中的流量。消息的生产者和消费者都使用冗余的预测历史来预测其内容。由于生产者知道消费者预测的结果，它只需要发送那些没有正确预测的数据。这种通信量的减少在通信比计算成本高得多的环境中是显著的，比如网络嵌入式系统。这三种研究途径代表了对基于预测的技术的新前沿的探索，从而产生了可扩展到新的可用性和性能水平的并行系统。

This research aims to expand and redefine the role of prediction-based techniques for parallel and distributed systems. First, we reduce barrier synchronization overhead by predicting the final producer of avalue before the barrier. This producer identification allows the consumer to speculatively proceed past the barrier, only waiting on the actual production as needed. Second, we introduce the slipstreamparadigm to multiprocessor systems. A redundant version of each parallel thread runs concurrently, its execution reduced by speculatively removing long-latency events, such as shared memory writes. The reduced thread dynamically detects sharing patterns, which are used by the original thread to optimize its coherence and synchronization actions, improving overall performance. Finally, we investigate the use of producer-validated message prediction to reduce traffic in a message-passing environment. Both the producer and the consumer of a message predict its contents, using redundant prediction histories. Since the producer knows the results of the consumer's prediction, it need only send those data that were not correctlypredicted. This traffic reduction is significant in environments in which communication is much more costly than computation, such as networked embedded systems. These three avenues of research represent an excursion into new frontiers of prediction-based technology, resulting in parallel systems that scale to new levels of availability and performance.