1,280 research outputs found
CuDB : a Relational Database Engine Boosted by Graphics Processing Units
Proceedings of the First PhD Symposium on Sustainable Ultrascale
Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.GPUs benefit from much more computation power with the same order of energy consumption than CPUs. Thanks
to their massive data parallel architecture, GPUs can outperform CPUs, especially on Single Program Multiple
Data (SPMD) programming paradigm on a large amount of data. Database engines are now everywhere, from
different sizes and complexities, for multiple usages, embedded or distributed; in 2012, 500 million of SQLite
active instances were estimated over the world. Our goal is to exploit the computation power of GPUs to improve
performance of SQLite, which is a key software component of many applications and systems. In this paper, we
introduce CuDB, a GPU-boosted in-memory database engine (IMDB) based on SQLite. The SQLite API remains
unchanged, allowing developers to easily upgrade database engine from SQlite to CuDB even on already existing
applications. Preliminary results show significant speedups of 70x with join queries on datasets of 1 million records.
We also demonstrate the "memory bounded" character of GPU-databases and show the energy efficiency of our
approach.European Cooperation in Science and Technology. COS
One size does not fit all : accelerating OLAP workloads with GPUs
GPU has been considered as one of the next-generation platforms for real-time query processing databases. In this paper we empirically demonstrate that the representative GPU databases [e.g., OmniSci (Open Source Analytical Database & SQL Engine,, 2019)] may be slower than the representative in-memory databases [e.g., Hyper (Neumann and Leis, IEEE Data Eng Bull 37(1):3-11, 2014)] with typical OLAP workloads (with Star Schema Benchmark) even if the actual dataset size of each query can completely fit in GPU memory. Therefore, we argue that GPU database designs should not be one-size-fits-all; a general-purpose GPU database engine may not be well-suited for OLAP workloads without careful designed GPU memory assignment and GPU computing locality. In order to achieve better performance for GPU OLAP, we need to re-organize OLAP operators and re-optimize OLAP model. In particular, we propose the 3-layer OLAP model to match the heterogeneous computing platforms. The core idea is to maximize data and computing locality to specified hardware. We design the vector grouping algorithm for data-intensive workload which is proved to be assigned to CPU platform adaptive. We design the TOP-DOWN query plan tree strategy to guarantee the optimal operation in final stage and pushing the respective optimizations to the lower layers to make global optimization gains. With this strategy, we design the 3-stage processing model (OLAP acceleration engine) for hybrid CPU-GPU platform, where the computing-intensive star-join stage is accelerated by GPU, and the data-intensive grouping & aggregation stage is accelerated by CPU. This design maximizes the locality of different workloads and simplifies the GPU acceleration implementation. Our experimental results show that with vector grouping and GPU accelerated star-join implementation, the OLAP acceleration engine runs 1.9x, 3.05x and 3.92x faster than Hyper, OmniSci GPU and OmniSci CPU in SSB evaluation with dataset of SF = 100.Peer reviewe
Deep Learning in the Automotive Industry: Applications and Tools
Deep Learning refers to a set of machine learning techniques that utilize
neural networks with many hidden layers for tasks, such as image
classification, speech recognition, language understanding. Deep learning has
been proven to be very effective in these domains and is pervasively used by
many Internet services. In this paper, we describe different automotive uses
cases for deep learning in particular in the domain of computer vision. We
surveys the current state-of-the-art in libraries, tools and infrastructures
(e.\,g.\ GPUs and clouds) for implementing, training and deploying deep neural
networks. We particularly focus on convolutional neural networks and computer
vision use cases, such as the visual inspection process in manufacturing plants
and the analysis of social media data. To train neural networks, curated and
labeled datasets are essential. In particular, both the availability and scope
of such datasets is typically very limited. A main contribution of this paper
is the creation of an automotive dataset, that allows us to learn and
automatically recognize different vehicle properties. We describe an end-to-end
deep learning application utilizing a mobile app for data collection and
process support, and an Amazon-based cloud backend for storage and training.
For training we evaluate the use of cloud and on-premises infrastructures
(including multiple GPUs) in conjunction with different neural network
architectures and frameworks. We assess both the training times as well as the
accuracy of the classifier. Finally, we demonstrate the effectiveness of the
trained classifier in a real world setting during manufacturing process.Comment: 10 page
Join Execution Using Fragmented Columnar Indices on GPU and MIC
The paper describes an approach to the parallel natural join execution on computing clusters with GPU and MIC Coprocessors. This approach is based on a decomposition of natural join relational operator using the column indices and domain-interval fragmentation. This decomposition admits parallel executing the resource-intensive relational operators without data transfers. All column index fragments are stored in main memory. To process the join of two relations, each pair of index fragments corresponding to particular domain interval is joined on a separate processor core. Described approach allows efficient parallel query processing for very large databases on modern computing cluster systems with many-core accelerators. A prototype of the DBMS coprocessor system was implemented using this technique. The results of computational experiments for GPU and Xeon Phi are presented. These results confirm the efficiency of proposed approach
Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)
Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to
present their current research, and to discuss topics with other students in order to look for synergies and common research
topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to
achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable
solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big
data management, training, contributing to glue disparate researchers working across different areas and provide a meeting
ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in
research topics such as sustainable software solutions (applications and system software stack), data management, energy
efficiency, and resilience.European Cooperation in Science and Technology. COS
A spatial column-store to triangulate the Netherlands on the fly
3D digital city models, important for urban planning, are currently constructed from massive point clouds obtained through airborne LiDAR (Light Detection and Ranging). They are semantically enriched with information obtained from auxiliary GIS data like Cadastral data which contains information about the boundaries of properties, road networks, rivers, lakes etc. Technical advances in the LiDAR data acquisition systems made possible the rapid acquisition of high resolution topographical information for an entire country. Such data sets are now reaching the trillion points barrier. To cope with this data deluge and provide up-to-date 3D digital city models on demand current geospatial management strategies should be re-thought. This work presents a column-oriented Spatial Database Management System which provides in-situ data access, effective data skipping, efficient spatial operations, and interactive data visualization. Its efficiency and scalability is demonstrated using a dense LiDAR scan of The Netherlands consisting of 640 billion points and the latest Cadastral information, and compared with PostGIS
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