Over the recent years, deep learning is widely being used in a variety of different fields and applications. The constant growth of data used to train complex models, has opened research in the distributed learning. In this domain, two main architectures are used to train models in a distribution fashion, all-reduce and parameter server. Both support synchronous learning, while parameter server also supports asynchronous learning. These architectures are adopted by tech companies, which have developed multiple systems for this purpose. Among the most popular and widely used distributed deep learning systems are Google TensorFlow, Facebook PyTorch and Apache MXNet. In this paper, we quantify the performance gap between these systems and present a detailed analysis to discuss the parameters that affect their execution time. Overall, in synchronous learning setups, TensorFlow is slower compared to PyTorch by average 2.65X, while the latter lags MXNet by average 1.38X. Regarding asynchronous learning, MXNet is faster by average 3.22X in respect with TensorFlow.

Over the last years, deep learning has gained an increase in popularity in various domains introducing complex models to handle the data explosion. However, while such model architectures can support the enormous amount of data, a single computing node cannot train the model using the whole data set in a timely fashion. Thus, specialized distributed architectures have been proposed, most of which follow data parallelism schemes, as the widely used parameter server approach. In this setup, each worker contributes to the training process in an asynchronous manner. While asynchronous training does not suffer from synchronization overheads, it introduces the problem of stale gradients which might cause the model to diverge during the training process. In this paper, we examine different data assignment schemes to workers, which facilitate the asynchronous learning approach. Specifically, we propose two different algorithms to perform the data sharding. Our experimental evaluation indicated that when stratification is taken into account the validation results present up to 6X less variance compared to standard sharding creation. When further data exploration for hidden stratification is performed, validation metrics can be slightly optimized. This method also achieves to reduce the variance of training and validation metrics by up to 8X and 2X respectively.

In this paper we present the SELIS Big Data Analytics and Machine Learning System (BDA), an open-source cloud-enabled elastic system that has been designed and implemented in order to address data related issues from the logistics domain. By taking into consideration real-life data analytics needs from more than 40 EU logistics providers we present the detailed SELIS BDA architecture along with the generic data and execution model devised to accommodate their diverse needs. We describe the main technologies we have utilized to realize the respective offering and justify our choices from the wider open-source Big Data systems community. We experimentally test our offering under various workloads where we prove that it can scale to serve a large number of concurrent requests while its abstraction/orchestration poses a very small overhead compared to the stand-alone Big Data systems. We believe that the SELIS BDA can be an easy-to-use entry point for the big data analytics world for any logistics company especially from the SME domain.

Over the past decade, a plethora of systems have emerged to support data analytics in various domains such as SQL and machine learning, among others. In each of the data analysis domains, there are now many different specialized systems that leverage domain-specific optimizations to efficiently execute their workloads. An alternative approach is to build a general-purpose data analytics system that uses a common execution engine and programming model to support workloads in different domains. In this work, we choose representative systems of each class (Spark, TensorFlow, Presto and Hive) and benchmark their performance on a wide variety of machine learning and SQL workloads. We perform an extensive comparative analysis on the strengths and limitations of each system and highlight major areas for improvement for all systems. We believe that the major insights gained from this study will be useful for developers to improve the performance of these systems.

Mobile Network Operators develop new technologies, as the 5G network, to handle the constantly increasing network traffic, while they put less effort on optimizing their operations. However, more energy efficient approaches are essential for cost reduction and compliance with energy footprint principles. Network usage and IoT data produced in the base stations can be used to develop such approaches. Considering the above, the BigOptiBase platform has been designed. Through this platform we will offer a big data analytics subsystem developed to provide elastic energy efficient solutions for the base stations using data analytics and machine learning technologies.

Nowadays, deep learning is a crucial part of a variety of big data applications. Both the vast amount of data and the high complexity of the state-of-the-art neural networks have led to perform the network training in a distributed manner accross clusters. Since synchronization overheads are usually fatal for the training's performance, asynchronous training is usually preferred in such cases. However, this training mode is sensitive to conflicting updates. Such updates most commonly occur when the workers train on a totally different part of the data. To reduce this phenomenon, in this paper, we propose the use of hashing schemes when distributing training data across workers.