Quantum technologies for future scientific research

• Simone Montangero (Universita' di Padova)

Room: C412 In this lesson, we briefly review the basics of quantum information theory and some of the most promising applications of quantum technologies for future research. In particular, we present the achievements and the challenges obtained by quantum simulators, dedicated quantum hardware built to simulate interesting but hardly accessible physics: from models to study for high-Tc superconductors or topological systems, critical systems, quantum chemistry or lattice gauge theories where Monte Carlo methods efficiency is hindered by the sign problem. In particular, we present the first experimental quantum simulations of lattice gauge theories. Alongside, we present the tensor network methods, a powerful classical numerical approach that promise to become a powerful tool accompanying future quantum simulations and computations, providing guidance, benchmarking and verification of the quantum results.

Scalable analysis of ultra-terabyte brain images: from low-level data management to deep learning

• Ludovico Silvestri (Istituto Nazionale di Ottica (INO-CNR))

Room: C412 Deciphering brain architecture at a system level requires the ability to quantitatively map its structure with cellular and subcellular resolution. Besides posing significant challenges to current optical microscopy methods, this ambitious goal require the development of a new generation of tools to make sense of the huge amount of raw images generated, which can easily exceeds several TeraBytes for a single sample. We present an integrated pipeline allowing the image transformation from a collection of voxel gray levels to a semantic representation of the sample. As a first step, the hundreds of adjacent tiles produced by the microscope needs to be aligned and fuse together. To this aim, we developed ZetaStitcher, a software for image stitching that computes global optimal alignment of imaging datasets as big as 8 TB in less than an hour. The fused volume is then generated virtually, without the need to create a physical copy of the dataset, by means of a dedicated API. The virtually fused volume is then processed to extract meaningful information. We demonstrate two complementary approaches based on deep convolutional networks. In one case, a 3D conv-net is used to ‘semantically deconvolve’ the image [Frasconi et al., Bioinformatics 2014], allowing accurate localization of neuronal bodies with standard clustering algorithms (e.g. mean shift). The scalability of this approach is demonstrated by mapping whole-brain spatial distribution of different neuronal populations with single-cell resolution. To go beyond simple localization, we exploited a 2D conv-net estimating for each pixel the probability of being part of a neuron [Mazzamuto et al., LNCS 2018]. The output of the net is then processed with a contour finding, obtaining reliable segmentation of cell morphology. This information can be used to classify neurons, expanding the potential of chemical labeling strategies.

ECMWF's Extreme Data Challenges for Exascale Numerical Weather Prediction

• Tiago Quintino (ECMWF)

Room: C412 Starting 2014, ECMWF has embarked on a 10 year research programme on HPC Scalability, aiming to achieve Exascale numerical weather prediction systems by 2025. ECMWF operational forecast generates massive amounts of I/O in short bursts, accumulating to tens of TiB in hourly windows. From this output, millions of user-defined daily products are generated and disseminated to member states and commercial clients all over the world. These products are processed from the raw output of the IFS model, within the time critical path and under strict delivery schedule. Upcoming rise in resolution and growing popularity will increase both the size and number of these products. Based on expected model resolution upgrades, by 2020 we estimate the operational model will output over 100 TiB/day and need to archive over 400 TiB/day. Given that the I/O workload is already one of the strongest bottlenecks in ECMWF's workflow, this is one of the main challenges to reach Exascale NWP. We present the latest ECMWF developments in model I/O and product generation, and how we are reworking our operational workflows to adapt to forthcoming new architectures and memory-storage hierarchies.

The challenge of Big Data in Science

• Dario Menasce (INFN Milano Bicocca)

Room: C412 In the last decade science has tackled problems in advanced research that required and provided a rapidly growing amount of data. Along with the size of these data, also their complexity has grown exponentially, as well as the time required to process and transport them across research centers worldwide. Availability of these data, their storage, the necessary computing power to extract meaning, the network to share them are all problems intertwined inextricably. Moreover, new technologies have surfaced, like multithreading, vectorization (and parallelism in general), along with methodologies such as Neural Networks, Artificial Intelligence and Quantum Computing. At the same time new computing languages are emerging, to cope with the necessity of allowing people to express complex constructs and algorithms in a natural and efficient way. In this seminar we will highlight a global picture of this emerging scenario and discuss the point of view of a researcher in this context.