分布式&并行&集群学术速递[1.10]
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cs.DC分布式&并行&集群，共计3篇
【1】 Multi-Model Federated Learning
标题：多模型联合学习
链接：https://arxiv.org/abs/2201.02582
摘要：Federated learning is a form of distributed learning with the key challenge
being the non-identically distributed nature of the data in the participating
clients. In this paper, we extend federated learning to the setting where
multiple unrelated models are trained simultaneously. Specifically, every
client is able to train any one of M models at a time and the server maintains
a model for each of the M models which is typically a suitably averaged version
of the model computed by the clients. We propose multiple policies for
assigning learning tasks to clients over time. In the first policy, we extend
the widely studied FedAvg to multi-model learning by allotting models to
clients in an i.i.d. stochastic manner. In addition, we propose two new
policies for client selection in a multi-model federated setting which make
decisions based on current local losses for each client-model pair. We compare
the performance of the policies on tasks involving synthetic and real-world
data and characterize the performance of the proposed policies. The key
take-away from our work is that the proposed multi-model policies perform
better or at least as good as single model training using FedAvg.
【2】 In Situ Data Summaries for Flexible Feature Analysis in Large-Scale Multiphase Flow Simulations
标题：大尺度多相流模拟中柔性特征分析的现场数据汇总
链接：https://arxiv.org/abs/2201.02557
摘要：The study of multiphase flow is essential for understanding the complex
interactions of various materials. In particular, when designing chemical
reactors such as fluidized bed reactors (FBR), a detailed understanding of the
hydrodynamics is critical for optimizing reactor performance and stability. An
FBR allows experts to conduct different types of chemical reactions involving
multiphase materials, especially interaction between gas and solids. During
such complex chemical processes, formation of void regions in the reactor,
generally termed as bubbles, is an important phenomenon. Study of these bubbles
has a deep implication in predicting the reactor's overall efficiency. But
physical experiments needed to understand bubble dynamics are costly and
non-trivial. Therefore, to study such chemical processes and bubble dynamics, a
state-of-the-art massively parallel computational fluid dynamics discrete
element model (CFD-DEM), MFIX-Exa is being developed for simulating multiphase
flows. Despite the proven accuracy of MFIX-Exa in modeling bubbling phenomena,
the very-large size of the output data prohibits the use of traditional post
hoc analysis capabilities in both storage and I/O time. To address these issues
and allow the application scientists to explore the bubble dynamics in an
efficient and timely manner, we have developed an end-to-end visual analytics
pipeline that enables in situ detection of bubbles using statistical
techniques, followed by a flexible and interactive visual exploration of bubble
dynamics in the post hoc analysis phase. Positive feedback from the experts has
indicated the efficacy of the proposed approach for exploring bubble dynamics
in very-large scale multiphase flow simulations.
【3】 A SIMD algorithm for the detection of epistatic interactions of any order
标题：检测任意阶上位性相互作用的SIMD算法
链接：https://arxiv.org/abs/2201.02460
备注：Submitted to Future Generation Computer Systems. Codes used are available at this https URL
摘要：Epistasis is a phenomenon in which a phenotype outcome is determined by the
interaction of genetic variation at two or more loci and it cannot be
attributed to the additive combination of effects corresponding to the
individual loci. Although it has been more than 100 years since William Bateson
introduced this concept, it still is a topic under active research. Locating
epistatic interactions is a computationally expensive challenge that involves
analyzing an exponentially growing number of combinations. Authors in this
field have resorted to a multitude of hardware architectures in order to speed
up the search, but little to no attention has been paid to the vector
instructions that current CPUs include in their instruction sets. This work
extends an existing third-order exhaustive algorithm to support the search of
epistasis interactions of any order and discusses multiple SIMD implementations
of the different functions that compose the search using Intel AVX Intrinsics.
Results using the GCC and the Intel compiler show that the 512-bit explicit
vector implementation proposed here performs the best out of all of the other
implementations evaluated. The proposed 512-bit vectorization accelerates the
original implementation of the algorithm by an average factor of 7 and 12, for
GCC and the Intel Compiler, respectively, in the scenarios tested.
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