The most significant source of lost performance when a thread migrates between cores is the loss of cache state. A significant boost in post-migration performance is possible if the cache working set can be moved, proactively, with the thread. This work accelerates thread startup performance after migration by predicting and prefetching the working set of the application into the new cache. It shows that simply moving cache state performs poorly, and that moving the instruction working set can be even more critical than data. This paper demonstrates a technique that captures the access behavior of a thread, summarizes that behavior into a compact form for transfer between cores, and then prefetches appropriate data into the new caches based on the summary. It presents a detailed study of single-thread migration effects, and then demonstrates its utility on a speculative multithreading architecture. Working set prediction as much as doubles the performance of short-lived threads, and in a full speculative multithreading implementation, the technique is also shown to nearly double the effectiveness of the spawned threads.

This paper describes initial results for an architecture called the Shared-Thread Multiprocessor (STMP). The STMP combines features of a multithreaded processor and a chip multiprocessor; specifically, it enables distinct cores on a chip multiprocessor to share thread state. This shared thread state allows the system to schedule threads from a shared pool onto individual cores, allowing for rapid movement of threads between cores.

As the number of cores increases on chip multiprocessors, coherence is fast becoming a central issue for multi-core performance. This is exacerbated by the fact that interconnection speeds are not scaling well with technology. This paper describes mechanisms to accelerate coherence for a multi-core architecture that has multiple private L2 caches and a scalable point-to-point interconnect between cores. These techniques exploit the differences in geometry between chip multiprocessors and traditional multiprocessor architectures.

Simultaneous multithreading architectures have been defined previously with fully shared execution resources. When one thread in such an architecture experiences a very long-latency operation, such as a load miss, the thread will eventually stall, potentially holding resources which other threads could be using to make forward progress. This paper shows that in many cases it is better to free the resources associated with a stalled thread rather than keep that thread ready to immediately begin execution upon return of the loaded data. Several possible architectures are examined, and some simple solutions are shown to be very effective, achieving speedups close to 6.0 in some cases, and averaging 15% speedup with four threads and over 100% speedup with two threads running. Response times are cut in half for several workloads in open system experiments.