4,333 research outputs found
DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives
We present a new parallel algorithm for probabilistic graphical model
optimization. The algorithm relies on data-parallel primitives (DPPs), which
provide portable performance over hardware architecture. We evaluate results on
CPUs and GPUs for an image segmentation problem. Compared to a serial baseline,
we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare
our performance to a reference, OpenMP-based algorithm, and find speedups of up
to 7X (CPU).Comment: LDAV 2018, October 201
Exploring the Performance Benefit of Hybrid Memory System on HPC Environments
Hardware accelerators have become a de-facto standard to achieve high
performance on current supercomputers and there are indications that this trend
will increase in the future. Modern accelerators feature high-bandwidth memory
next to the computing cores. For example, the Intel Knights Landing (KNL)
processor is equipped with 16 GB of high-bandwidth memory (HBM) that works
together with conventional DRAM memory. Theoretically, HBM can provide 5x
higher bandwidth than conventional DRAM. However, many factors impact the
effective performance achieved by applications, including the application
memory access pattern, the problem size, the threading level and the actual
memory configuration. In this paper, we analyze the Intel KNL system and
quantify the impact of the most important factors on the application
performance by using a set of applications that are representative of
scientific and data-analytics workloads. Our results show that applications
with regular memory access benefit from MCDRAM, achieving up to 3x performance
when compared to the performance obtained using only DRAM. On the contrary,
applications with random memory access pattern are latency-bound and may suffer
from performance degradation when using only MCDRAM. For those applications,
the use of additional hardware threads may help hide latency and achieve higher
aggregated bandwidth when using HBM
Evaluating the benefits of key-value databases for scientific applications
The convergence of Big Data applications with High-Performance Computing requires new methodologies to store, manage and process large amounts of information. Traditional storage solutions are unable to scale and that results in complex coding strategies. For example, the brain atlas of the Human Brain Project has the challenge to process large amounts of high-resolution brain images. Given the computing needs, we study the effects of replacing a traditional storage system with a distributed Key-Value database on a cell segmentation application. The original code uses HDF5 files on GPFS through an intricate interface, imposing synchronizations. On the other hand, by using Apache Cassandra or ScyllaDB through Hecuba, the application code is greatly simplified. Thanks to the Key-Value data model, the number of synchronizations is reduced and the time dedicated to I/O scales when increasing the number of nodes.This project/research has received funding from the European Unions Horizon
2020 Framework Programme for Research and Innovation under the Speci c
Grant Agreement No. 720270 (Human Brain Project SGA1) and the Speci c
Grant Agreement No. 785907 (Human Brain Project SGA2). This work has also
been supported by the Spanish Government (SEV2015-0493), by the Spanish
Ministry of Science and Innovation (contract TIN2015-65316-P), and by Generalitat
de Catalunya (contract 2017-SGR-1414).Postprint (author's final draft
Enhancing Energy Production with Exascale HPC Methods
High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose
processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale
simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of
Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and
from the Brazilian Ministry of Science, Technology and Innovation through Rede
Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the
Intel Corporation, which enabled us to obtain the presented experimental results in
uncertainty quantification in seismic imagingPostprint (author's final draft
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