27,111 research outputs found
NoCo: ILP-based worst-case contention estimation for mesh real-time manycores
Manycores are capable of providing the computational demands required by functionally-advanced critical applications in domains such as automotive and avionics. In manycores a network-on-chip (NoC) provides access to shared caches and memories and hence concentrates most of the contention that tasks suffer, with effects on the worst-case contention delay (WCD) of packets and tasks' WCET. While several proposals minimize the impact of individual NoC parameters on WCD, e.g. mapping and routing, there are strong dependences among these NoC parameters. Hence, finding the optimal NoC configurations requires optimizing all parameters simultaneously, which represents a multidimensional optimization problem. In this paper we propose NoCo, a novel approach that combines ILP and stochastic optimization to find NoC configurations in terms of packet routing, application mapping, and arbitration weight allocation. Our results show that NoCo improves other techniques that optimize a subset of NoC parameters.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under grant TIN2015-
65316-P and the HiPEAC Network of Excellence. It also received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (agreement No. 772773). Carles Hernández
is jointly supported by the MINECO and FEDER funds
through grant TIN2014-60404-JIN. Jaume Abella has been
partially supported by the Spanish Ministry of Economy and
Competitiveness under Ramon y Cajal postdoctoral fellowship
number RYC-2013-14717. Enrico Mezzetti has been partially
supported by the Spanish Ministry of Economy and Competitiveness
under Juan de la Cierva-Incorporaci´on postdoctoral
fellowship number IJCI-2016-27396.Peer ReviewedPostprint (author's final draft
Scaling Deep Learning on GPU and Knights Landing clusters
The speed of deep neural networks training has become a big bottleneck of
deep learning research and development. For example, training GoogleNet by
ImageNet dataset on one Nvidia K20 GPU needs 21 days. To speed up the training
process, the current deep learning systems heavily rely on the hardware
accelerators. However, these accelerators have limited on-chip memory compared
with CPUs. To handle large datasets, they need to fetch data from either CPU
memory or remote processors. We use both self-hosted Intel Knights Landing
(KNL) clusters and multi-GPU clusters as our target platforms. From an
algorithm aspect, current distributed machine learning systems are mainly
designed for cloud systems. These methods are asynchronous because of the slow
network and high fault-tolerance requirement on cloud systems. We focus on
Elastic Averaging SGD (EASGD) to design algorithms for HPC clusters. Original
EASGD used round-robin method for communication and updating. The communication
is ordered by the machine rank ID, which is inefficient on HPC clusters.
First, we redesign four efficient algorithms for HPC systems to improve
EASGD's poor scaling on clusters. Async EASGD, Async MEASGD, and Hogwild EASGD
are faster \textcolor{black}{than} their existing counterparts (Async SGD,
Async MSGD, and Hogwild SGD, resp.) in all the comparisons. Finally, we design
Sync EASGD, which ties for the best performance among all the methods while
being deterministic. In addition to the algorithmic improvements, we use some
system-algorithm codesign techniques to scale up the algorithms. By reducing
the percentage of communication from 87% to 14%, our Sync EASGD achieves 5.3x
speedup over original EASGD on the same platform. We get 91.5% weak scaling
efficiency on 4253 KNL cores, which is higher than the state-of-the-art
implementation
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