Optimizing NEURON Simulation Environment Using Remote Memory Access with Recursive Doubling on Distributed Memory Systems

Increase in complexity of neuronal network models escalated the efforts to make NEURON simulation environment efficient. The computational neuroscientists divided the equations into subnets amongst multiple processors for achieving better hardware performance. On parallel machines for neuronal networks, interprocessor spikes exchange consumes large section of overall simulation time. In NEURON for communication between processors Message Passing Interface (MPI) is used. MPI_Allgather collective is exercised for spikes exchange after each interval across distributed memory systems. The increase in number of processors though results in achieving concurrency and better performance but it inversely affects MPI_Allgather which increases communication time between processors. This necessitates improving communication methodology to decrease the spikes exchange time over distributed memory systems. This work has improved MPI_Allgather method using Remote Memory Access (RMA) by moving two-sided communication to one-sided communication, and use of recursive doubling mechanism facilitates achieving efficient communication between the processors in precise steps. This approach enhanced communication concurrency and has improved overall runtime making NEURON more efficient for simulation of large neuronal network models

Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included

Proceedings of the NASA Conference on Space Telerobotics, volume 2

These proceedings contain papers presented at the NASA Conference on Space Telerobotics held in Pasadena, January 31 to February 2, 1989. The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research

Improving scalability of large-scale distributed Spiking Neural Network simulations on High Performance Computing systems using novel architecture-aware streaming hypergraph partitioning

After theory and experimentation, modelling and simulation is regarded as the third pillar of science, helping scientists to further their understanding of a complex system. In recent years there has been a growing scientific focus on computational neuroscience as a means to understand the brain and its functions, with large international projects (Human Brain Project, Brain Activity Map, MindScope and \textit{China Brain Project}) aiming to further our knowledge of high level cognitive functions. They are a testament to the enormous interest, difficulty and importance of solving the mysteries of the brain. Spiking Neural Network (SNN) simulations are widely used in the domain to facilitate experimentation. Scaling SNN simulations to large networks usually results in more-than-linear increase in computational complexity. The computing resources required at the brain scale simulation far surpass the capabilities of personal computers today. If those demands are to be met, distributed computation models need to be adopted, since there is a slow down of improvements in individual processors speed due to physical limitations on heat dissipation. This is a significant change that requires careful management of the workload in many levels: partition of work, communication and workload balancing, efficient inter-process communication and efficient use of available memory. If large scale neuronal network models are to be run successfully, simulators must consider these, and offer a viable solution to the challenges they pose. Large scale SNN simulations evidence most of the issues of general HPC systems evident in large distributed computation. Commonly used distribution of workload algorithms (round robin, random and manual allocation) do not take into consideration connectivity locality, which is natural in biological networks, which can lead to increased communication requirements when distributing the simulation in multiple computing nodes. State-of-the-art SNN simulations use dense communication collectives to distribute spike data. The common method of point to point communication in distributed computation is through dense patterns. Sparse communication collectives have been suggested to incur in lower overheads when the application's pattern of communication is sparse. In this work we characterise the bottlenecks on communication-bound SNN simulations and identify communication balance and sparsity as the main contributors to scalability. We propose hypergraph partitioning to distribute neurons along computing nodes to minimise communication (increasing sparsity). A hypergraph is a generalisation of graphs, where a (hyper)edge can link 2 or more vertices at once. Coupled with a novel use of sparse-aware communication collective, computational efficiency increases by up to 40.8 percent points and simulation time reduces by up to 73\%, compared to the common round-robin allocation in neuronal simulators. HPC systems have, by design, highly hierarchical communication network links, with qualitative differences in communication speed and latency between computing nodes. This can create a mismatch between the distributed simulation communication patterns and the physical capabilities of the hardware. If large distributed simulations are to take full advantage of these systems, the communication properties of the HPC need to be taken into consideration when allocating workload to route frequent, heavy communication through fast network links. Strategies that consider the heterogeneous physical communication capabilities are called architecture-aware. After demonstrating that hypergraph partitioning leads to more efficient workload allocation in SNN simulations, this thesis proposes a novel sequential hypergraph partitioning algorithm that incorporates network bandwidth via profiling. This leads to a significant reduction in execution time (up to 14x speedup in synthetic benchmark simulations compared to architecture-agnostic partitioners). The motivating context of this work is large scale brain simulations, however in the era of social media, large graphs and hypergraphs are increasingly relevant in many other scientific applications. A common feature of such graphs is that they are too big for a single machine to cope, both in terms of performance and memory requirements. State-of-the-art multilevel partitioning has been shown to struggle to scale to large graphs in distributed memory, not just because they take a long time to process, but also because they require full knowledge of the graph (not possible in dynamic graphs) and to fit the graph entirely in memory (not possible for very large graphs). To address those limitations we propose a parallel implementation of our architecture-aware streaming hypergraph partitioning algorithm (HyperPRAW) to model distributed applications. Results demonstrate that HyperPRAW produces consistent speedup over previous streaming approaches that only consider hyperedge overlap (up to 5.2x speedup). Compared to multilevel global partitioner in dense hypergraphs (those with high average cardinality), HyperPRAW is able to produce workload allocations that result in speeding up runtime in a synthetic simulation benchmark (up to 4.3x). HyperPRAW has the potential to scale to very large hypergraphs as it only requires local information to make allocation decisions, with an order of magnitude less memory footprint than global partitioners. The combined contributions of this thesis lead to a novel, parallel, scalable, streaming hypergraph partitioning algorithm (HyperPRAW) that can be used to help scale large distributed simulations in HPC systems. HyperPRAW helps tackle three of the main scalability challenges: it produces highly balanced distributed computation and communication, minimising idle time between computing nodes; it reduces the communication overhead by placing frequently communicating simulation elements close to each other (where the communication cost is minimal); and it provides a solution with a reasonable memory footprint that allows tackling larger problems than state-of-the-art alternatives such as global multilevel partitioning

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Koneoppimispohjainen ennakoiva keilanvalinta nopeasti liikkuville käyttäjille mMIMO-systeemeissä

The amount of mobile subscribers is growing each year and service is constantly required in increasingly difficult conditions. Notably, high-speed trains are an example of an environment where the extremely high velocities cause difficulties in obtaining sufficient signal quality. As the user equipment (UE) is constantly changing its position, the base station (BS) must adapt to this movement and predict the transmission direction in advance to mitigate the loss in signal quality. In this thesis, we study the application of machine learning algorithms for predictive beam selection. Beam selection is a process where the BS selects a suitable downlink beam out of a finite set of beams, which is called a grid of beams (GoB). We create a simulation environment where UEs move along a pre-defined path with scattering mirrors placed in random locations and measure the received signal gain in the downlink direction. The baseline algorithm is defined as a persistent model, in which the BS uses the optimal beam based on the feedback from the UE from the previous time step for downlink transmission. The baseline performance is compared with Long short-term memory (LSTM), Multi-layer perceptron (MLP), Support vector machine (SVM), Naive Bayes (NB) and Kalman filter (KF). In the experiments, we find that the baseline algorithm performance deteriorates when UE velocity, or number of scatterers or antennas is increased. When machine learning is used for predictive beam selection, the achieved gain averaged over velocities from 100 to 1500 km/h is around 2-35% higher compared to the baseline, depending on the number of scatterers and antennas. We also provide results of the empirical time complexities of the algorithms, allowing comparison between accuracy and time complexity. The results are promising, but further research is required to validate the concept in real-world communication systems.Mobiiliverkkojen käyttäjämäärä kasvaa jatkuvasti ja palvelua vaaditaan yhä vaativammissa ympäristöissä. Esimerkiksi luotijunissa suuret nopeudet hankaloittavat riittävän vahvan signaalin tarjoamista. Kun käyttäjät vaihtavat sijaintiaan jatkuvasti, täytyy tukiaseman sopeutua tähän liikkeeseen ja ennakoida lähetyssuunta, jotta signaalinlaatu pysyy halutulla tasolla. Tässä diplomityössä tutkitaan koneoppimismenetelmien soveltamista ennakoivaan keilanvalintaan. Keilanvalinnassa tukiasema valitsee sopivan lähetyskeilan mahdollisten keilojen joukosta. Kokeellisessa osuudessa luomme sirottavia peilejä sisältävän simuloidun ympäristön, jossa käyttäjät liikkuvat ennalta määritettyä polkua pitkin. Käyttäjät mittaavat signaalinvoimakkuutta tietyin väliajoin ja raportoivat sopivan lähetyskeilan tukiasemalle. Koneoppimisalgoritmien suorituskykyä keilanvalinnassa verrataan malliin, jossa edellisen ajanhetken mittausten perusteella voimakkain keila valitaan käytettäväksi seuraavassa mittauspisteessä. Vertailtavat koneoppimisalgoritmit ovat Long short-term memory -verkko (LSTM), monikerroksinen perseptroniverkko (MLP), tukivektorikone (SVM), Naiivi Bayesin luokitin (NB) ja Kalman-suodin (KF). Tuloksista nähdään, että verrokkimallin suorituskyky heikentyy, kun käyttäjien nopeutta tai peilien tai tukiaseman antennien määrää kasvatetaan. Koneoppimisalgoritmeillä saavutetaan 2-35% verrokkimallia suurempi signaalinvoimakkuus, kun tarkastellaan keskiarvoistettuja tuloksia 100 ja 1500 km/h nopeuksien välillä. Tuloksissa tarkastelemme myös algoritmien suoritusaikoja, mikä mahdollistaa vertailun mallien tarkkuuden ja aikakompleksisuuden välillä. Tulokset ovat lupaavia, mutta lisätutkimusta vaaditaan konseptin toimivuuden varmentamiseksi oikeissa mobiiliverkoissa

Technology 2003: The Fourth National Technology Transfer Conference and Exposition, volume 2

Proceedings from symposia of the Technology 2003 Conference and Exposition, Dec. 7-9, 1993, Anaheim, CA, are presented. Volume 2 features papers on artificial intelligence, CAD&E, computer hardware, computer software, information management, photonics, robotics, test and measurement, video and imaging, and virtual reality/simulation

Measurements, Models, Systems and Design

531 s.

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest