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Explicit-multithreading (XMT) is a parallel programming approach for exploiting on-chip parallelism. XMT introduces a computational framework with 1) a simple programming style that relies on fine-grained PRAM-style algorithms; 2) hardware support for low-overhead parallel threads, scalable load balancing, and efficient synchronization. The missing link between the algorithmic-programming level and the architecture level is provided by the first prototype XMT compiler. This paper also takes this new opportunity to evaluate the overall effectiveness of the interaction between the programming model and the hardware, and enhance its performance where needed, incorporating new optimizations into the XMT compiler. We present a wide range of applications, which written in XMT obtain significant speedups relative to the best serial programs. We show that XMT is especially useful for more advanced applications with dynamic, irregular access pattern, where for regular computations we demonstrate performance gains that scale up to much higher levels than have been demonstrated before for on-chip systems.

This paper envisions an extension to a standard instruction set which efficiently implements PRAM-style algorithms using explicit multi-threaded instruction-level parallelism (ILP); that is, ExplicitMulti-Threading (XMT), a fine-grained computational paradigm covering the spectrum from algorithms through architecture to implementation is introduced; new elements are added where needed.

Increasing transistor density enables adding more on-die cache real-estate. However, devoting more space to the shared lastlevel-cache (LLC) causes the memory latency bottleneck to move from memory access latency to shared cache access latency. As such, applications whose working set is larger than the smaller caches spend a large fraction of their execution time on shared cache access latency. To address this problem, this paper investigates increasing the size of smaller private caches in the hierarchy as opposed to increasing the shared LLC. Doing so improves average cache access latency for workloads whose working set fits into the larger private cache while retaining the benefits of a shared LLC. The consequence of increasing the size of private caches is to relax inclusion and build exclusive hierarchies. Thus, for the same total caching capacity, an exclusive cache hierarchy provides better cache access latency.We observe that server workloads benefit tremendously from an exclusive hierarchy with large private caches. This is primarily because large private caches accommodate the large code workingsets of server workloads. For a 16-core CMP, an exclusive cache hierarchy improves server workload performance by 5-12% as compared to an equal capacity inclusive cache hierarchy. The paper also presents directions for further research to maximize performance of exclusive cache hierarchies.

Explicit-multithreading (XMT) is a parallel programming model designed for exploiting on-chip parallelism. Its features include a simple thread execution model and an efficient prefix-sum instruction for synchronizing shared data accesses. By taking advantage of low-overhead parallel threads and high on-chip memory bandwidth, the XMT model tries to reduce the burden on programmers by obviating the need for explicit task assignment and thread coarsening. This paper presents features of the XMT programming model, and evaluates their utility through experiments on a prototype XMT compiler and architecture simulator. We find the lack of explicit task assignment has slight effects on performance for the XMT architecture. Despite low thread overhead, thread coarsening is still necessary to some extent, but can usually be automatically applied by the XMT compiler. The prefix-sum instruction provides more scalable synchronization than traditional locks, and the simple run-until-completion thread execution model (no busywaits) does not impair performance. Finally, the combination of features in XMT can encourage simpler parallel algorithms that may be more efficient than more traditional complex approaches.