6,317 research outputs found
A software-hardware hybrid steering mechanism for clustered microarchitectures
Clustered microarchitectures provide a promising paradigm to solve or alleviate the problems of increasing microprocessor complexity and wire delays. High- performance out-of-order processors rely on hardware-only steering mechanisms to achieve balanced workload distribution among clusters. However, the additional steering logic results in a significant increase on complexity, which actually decreases the benefits of the clustered design. In this paper, we address this complexity issue and present a novel software-hardware hybrid steering mechanism for out-of-order processors. The proposed software- hardware cooperative scheme makes use of the concept of virtual clusters. Instructions are distributed to virtual clusters at compile time using static properties of the program such as data dependences. Then, at runtime, virtual clusters are mapped into physical clusters by considering workload information. Experiments using SPEC CPU2000 benchmarks show that our hybrid approach can achieve almost the same performance as a state-of-the-art hardware-only steering scheme, while requiring low hardware complexity. In addition, the proposed mechanism outperforms state-of-the-art software-only steering mechanisms by 5% and 10% on average for 2-cluster and 4-cluster machines, respectively.Peer ReviewedPostprint (published version
Adaptive runtime-assisted block prefetching on chip-multiprocessors
Memory stalls are a significant source of performance degradation in modern processors. Data prefetching is a widely adopted and well studied technique used to alleviate this problem. Prefetching can be performed by the hardware, or be initiated and controlled by software. Among software controlled prefetching we find a wide variety of schemes, including runtime-directed prefetching and more specifically runtime-directed block prefetching. This paper proposes a hybrid prefetching mechanism that integrates a software driven block prefetcher with existing hardware prefetching techniques. Our runtime-assisted software prefetcher brings large blocks of data on-chip with the support of a low cost hardware engine, and synergizes with existing hardware prefetchers that manage locality at a finer granularity. The runtime system that drives the prefetch engine dynamically selects which cache to prefetch to. Our evaluation on a set of scientific benchmarks obtains a maximum speed up of 32 and 10 % on average compared to a baseline with hardware prefetching only. As a result, we also achieve a reduction of up to 18 and 3 % on average in energy-to-solution.Peer ReviewedPostprint (author's final draft
Reproducibility, accuracy and performance of the Feltor code and library on parallel computer architectures
Feltor is a modular and free scientific software package. It allows
developing platform independent code that runs on a variety of parallel
computer architectures ranging from laptop CPUs to multi-GPU distributed memory
systems. Feltor consists of both a numerical library and a collection of
application codes built on top of the library. Its main target are two- and
three-dimensional drift- and gyro-fluid simulations with discontinuous Galerkin
methods as the main numerical discretization technique. We observe that
numerical simulations of a recently developed gyro-fluid model produce
non-deterministic results in parallel computations. First, we show how we
restore accuracy and bitwise reproducibility algorithmically and
programmatically. In particular, we adopt an implementation of the exactly
rounded dot product based on long accumulators, which avoids accuracy losses
especially in parallel applications. However, reproducibility and accuracy
alone fail to indicate correct simulation behaviour. In fact, in the physical
model slightly different initial conditions lead to vastly different end
states. This behaviour translates to its numerical representation. Pointwise
convergence, even in principle, becomes impossible for long simulation times.
In a second part, we explore important performance tuning considerations. We
identify latency and memory bandwidth as the main performance indicators of our
routines. Based on these, we propose a parallel performance model that predicts
the execution time of algorithms implemented in Feltor and test our model on a
selection of parallel hardware architectures. We are able to predict the
execution time with a relative error of less than 25% for problem sizes between
0.1 and 1000 MB. Finally, we find that the product of latency and bandwidth
gives a minimum array size per compute node to achieve a scaling efficiency
above 50% (both strong and weak)
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