Learn how NVIDIA's GPUs are used to accelerate unified communications (UC) analytics processing by mathematically classifying UC call flows. We'll discuss how we're leveraging NVIDIA GPU parallelization technology to support classification and baselining of UC call flows, protect UC against fraudulent attacks, and establish predictive UC forecasting models. We'll explain how this allows us to more accurately identify and forecast deviations that may represent malicious use of UC against a baseline of normal traffic.  Back

We'll discuss the Bayesian statistical paradigm and Markov Chain Monte Carlo (MCMC) algorithms - the cornerstone of modern Bayesian computation. Scalable MCMC for big datasets and complex models is currently an open research question. Using GPUs provides a promising and largely unexplored avenue for accelerating these algorithms, but is nontrivial, because MCMC is inherently sequential and has traditionally been considered difficult to parallelize. We'll show how Gibbs sampling, a widely used MCMC algorithm, can be effectively parallelized on GPUs for a large class of exchangeable hierarchical Bayesian models. Participants will learn the mathematical and hardware/software challenges in bringing GPUs to the Bayesian community. Background in Bayesian statistics or MCMC is not assumed.  Back

Have you heard about the world's biggest eye ever built? Are you interested in scientific simulations running on NVIDIA DGX-1? Come and learn how combining these powerful computing devices dramatically leaps forward the computational astronomy community in designing major, multimillion-dollar optical instruments for the European Extremely Large Telescope. Starting from the mathematical model up to the high-performance implementation on DGX-1, we'll explain how the resulting matrix computations associated with an efficient task-based programming model help design the next generation of telescope instruments and, eventually, demonstrate a pathfinder for the discovery of new galaxies.  Back

Have you heard about the largest ground-based telescope ever built? Are you interested in the newest NVIDIA DGX-1 hardware accelerator? Come and learn how the DGX-1 architecture dramatically leaps forward the computational astronomy community in designing major, multimillion-dollar optical instruments for the European Extremely Large Telescope. Starting from the mathematical model up to the high-performance implementation on distributed-memory systems with hardware accelerators, we'll explain how the resulting matrix computations associated with an efficient task-based programming model help design the next generation of telescope instruments.

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The greatest challenge regarding simulation is finding the matrix that compares it to real life. In this session, Danny will present a mathematical matrix that defines this relation and show how a proper simulation can be constructed based on deep learning techniques. He will also supply live examples of the Cognata simulation platform.

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When used as predictive tools in natural disasters such as tsunamis, numerical models require extremely fast computations. Just a few years ago, real-time computing in Tsunami Early Warning Systems (TEWS) was unthinkable. Nevertheless, the EDANYA Group has revolutionized tsunami science paradigms. With the goal of saving lives in the framework of TEWS, our group has developed Tsunami-HySEA, a GPU-based numerical model aimed at producing numerical simulations of tsunami events that are faster than ever. Based on highly efficient, robust mathematical algorithms, together with the computational power of NVIDIA GPUs, Tsunami-HySEA is able to simulate a tsunami event in only a few minutes. Nowadays, one of the main challenges in tsunami science is producing accurate assessments of tsunami wave impacts and just a few minutes after the generating earthquake is triggered. This timely prediction would save many lives in a tsunami scenario. When the response is needed only in a few minutes, the resulting scenario is challenging. The required characteristics are difficult to combine in a single numerical tool: robustness, low-dissipation, large domains, and an extremely fast response  Back

We'll discuss cuTENSOR, a high-performance CUDA library for tensor operations that efficiently handles the ubiquitous presence of high-dimensional arrays (i.e., tensors) in today's HPC and DL workloads. This library supports highly efficient tensor operations such as tensor contractions (a generalization of matrix-matrix multiplications), point-wise tensor operations such as tensor permutations, and tensor decompositions (a generalization of matrix decompositions). While providing high performance, cuTENSOR also allows users to express their mathematical equations for tensors in a straightforward way that hides the complexity of dealing with these high-dimensional objects behind an easy-to-use API. CUDA 10.1 enables CUDA programmers to utilize Tensor Cores directly with the new mma.sync instruction. In this presentation, we describe the functionality of mma.sync and present strategies for implementing efficient matrix multiply computations in CUDA that maximize performance on NVIDIA Volta GPUs. We then describe how CUTLASS 1.3 provides reusable components embodying these strategies. CUTLASS 1.3 demonstrates a median 44% speedup of CUDA kernels executing layers from real-world Deep Learning workloads.  Back

The goal of this lab is to give a guided tour through the essentials of CUDA parallelization in mathematical finance. We consider pricing a bullet option under LV (Local Volatility) model using either MC (Monte Carlo) or an implicit discretization scheme for PDE (Partial Differential Equation). The considered example is close to a real banking application with an LV model derived from the implicit SVI model of Gatheral & Jacquier using the Andersen & Brotherton-Ratcliffe expression based on Dupire equation. Several optimizations are studied like the judicious memory storage in shared and registers for two discretization scales in MC. Thanks to a simple trick proposed in Abbas-Turki & Graillat, we see also the use of PCR (Parallel Cyclic Reduction) to solve tridiagonal systems of any size.

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We'll discuss our work using neural networks to fit the interatomic potential function and describe how we tested the network's potential function in atomic simulation software. This method has lower computational cost than traditional density functional theory methods. We'll show how our work is applicable to different atom types and architectures and how it avoids relying on the physical model. Instead, it uses a purely mathematical representation, which reduces the need for human intervention.  Back

We introduce cuTENSOR, a high-performance CUDA library for tensor operations that efficiently handles the ubiquitous presence of high-dimensional arrays (i.e., tensors) in today's HPC and DL workloads. This library supports highly efficient tensor operations such as tensor contractions (a generalization of matrix-matrix multiplications), element-wise tensor operations such as tensor permutations, and tensor reductions. While providing high performance, cuTENSOR also allows users to express their mathematical equations for tensors in a straight-forward way that hides the complexity of dealing with these high-dimensional objects behind an easy-to-use API.  Back

The talk presents the current developments of the NUMBERS project highly interdisciplinary research program in Cognitive Developmental Robotics to construct a novel artificial cognitive model of mathematical cognition that imitates human-like learning approaches for developing number understanding.
The project aims to provide a proof-of-concept and the scientific and technological bases for novel robots capable of abstract and symbolic processing, which is required for improving their cognitive performance and their social interaction with human beings.
During the talk, the current experimental results will be reviewed to give evidence of improved performance thanks to the embodiment.

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What do we need to achieve artificial general intelligence? How do we distribute intelligence over the internet-of-things? We'll dive deep into the heart of the matter, which is machine reasoning. Following recent advances in mathematical foundations and homotopy-type theory, we conclude that the crux is to formally separate intents from implementations. We can teach neural networks to understand these intents and to use a divide-and-conquer method for compiling these intents into implementations. Our goal is to outline a distributed strategy for accomplishing this moonshot.  Back