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As parallel jobs get bigger in size and finer in granularity, "system noise" is increasingly becoming a problem. In fact, fine-grained jobs on clusters with thousands of SMP nodes run faster if a processor is intentionally left idle (per node), thus enabling a separation of "system noise" from the computation. Paying a cost in average processing speed at a node for the sake of eliminating occasional processes delays is (unfortunately) beneficial, as such delays are enormously magnified when one late process holds up thousands of peers with which it synchronizes.We provide a probabilistic argument showing that, under certain conditions, the effect of such noise is linearly proportional to the size of the cluster (as is often empirically observed). We then identify a major source of noise to be indirect overhead of periodic OS clock interrupts ("ticks"), that are used by all general-purpose OSs as a means of maintaining control. This is shown for various grain sizes, platforms, tick frequencies, and OSs. To eliminate such noise, we suggest replacing ticks with an alternative mechanism we call "smart timers". This turns out to also be in line with needs of desktop and mobile computing, increasing the chances of the suggested change to be accepted.

Abstract-We present Task Superscalar, an abstraction of instruction-level out-of-order pipeline that operates at the tasklevel. Like ILP pipelines, which uncover parallelism in a sequential instruction stream, task superscalar uncovers tasklevel parallelism among tasks generated by a sequential thread. Utilizing intuitive programmer annotations of task inputs and outputs, the task superscalar pipeline dynamically detects intertask data dependencies, identifies task-level parallelism, and executes tasks out-of-order.Furthermore, we propose a design for a distributed task superscalar pipeline frontend, that can be embedded into any manycore fabric, and manages cores as functional units.We show that our proposed mechanism is capable of driving hundreds of cores simultaneously with non-speculative tasks, which allows our pipeline to sustain work windows consisting of tens of thousands of tasks. We further show that our pipeline can maintain a decode rate faster than 60ns per task and dynamically uncover data dependencies among as many as ∼50,000 in-flight tasks, using 7MB of on-chip eDRAM storage. This configuration achieves speedups of 95-255x (average 183x) over sequential execution for nine scientific benchmarks, running on a simulated CMP with 256 cores.Task superscalar thus enables programmers to exploit manycore systems effectively, while simultaneously simplifying their programming model.

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Most modern memory prefetchers rely on spatio-temporal locality to predict the memory addresses likely to be accessed by a program in the near future. Emerging workloads, however, make increasing use of irregular data structures, and thus exhibit a lower degree of spatial locality. This makes them less amenable to spatio-temporal prefetchers.In this paper, we introduce the concept of Semantic Locality, which uses inherent program semantics to characterize access relations. We show how, in principle, semantic locality can capture the relationship between data elements in a manner agnostic to the actual data layout, and we argue that semantic locality transcends spatio-temporal concerns.We further introduce the context-based memory prefetcher, which approximates semantic locality using reinforcement learning. The prefetcher identifies access patterns by applying reinforcement learning methods over machine and code attributes, that provide hints on memory access semantics.We test our prefetcher on a variety of benchmarks that employ both regular and irregular patterns. For the SPEC 2006 suite, it delivers speedups as high as 2.8× (20% on average) over a baseline with no prefetching, and outperforms leading spatio-temporal prefetchers. Finally, we show that the contextbased prefetcher makes it possible for naive, pointer-based implementations of irregular algorithms to achieve performance comparable to that of spatially optimized code.