Computational science | EPFL Graph Search

Computational science, also known as scientific computing, technical computing or scientific computation (SC), is a division of science that uses advanced computing capabilities to understand and solve complex physical problems. This includes

Algorithms (numerical and non-numerical): mathematical models, computational models, and computer simulations developed to solve sciences (e.g, physical, biological, and social), engineering, and humanities problems
Computer hardware that develops and optimizes the advanced system hardware, firmware, networking, and data management components needed to solve computationally demanding problems
The computing infrastructure that supports both the science and engineering problem solving and the developmental computer and information science In practical use, it is typically the application of computer simulation and other forms of computation from numerical analysis and theoretical computer science to solve problems in various scientific disci

Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate three topics: the automation of routines, the organization of artificial intelligence (AI), and the evaluation of technology risk. Overall, I argue that new technologies are not a panacea for the firm but require deliberate strategic planning to manage the potential downsides of myopic automation, AI interdependencies, and the disclosure of technology risks.In the first essay, I argue that while automation can increase productivity by reducing the costs of coordinating individuals, the automation of routines can also incur an indirect opportunity cost due to slow adaptation to environmental change. I develop a reinforcement learning simulation to model the impact of automation on the returns from the division of labor in dynamic environments and to show how automation incurs opportunity costs through lost learning and slow adaptation. Moreover, automation can be suboptimal when it brings about myopic behavior, i.e. high returns from the division of labor in the short term, but negative returns in the long term. Given the simulation results, I argue that firms need dynamic routines to simultaneously balance learning and automation. I open-source the simulation platform as OrgSim-RL on GitHub.In the second essay, I argue that a data-driven culture - what I define as a Data Clan - can help to coordinate complex interdependencies between AI components within a firm. I analyze in-depth semi-structured interview data with a hierarchical stochastic block model (hSBM) and hand-coding to find that managers focus primarily on building a strong culture and establishing high-quality data assets when allocating resources to AI initiatives. Given the results, I inductively develop implications for theory and argue that the emergence of a Data Clan can be a governance mechanism to reduce coordination frictions and build a competitive advantage in the age of AI.In the third essay, I argue that investors require a higher initial return to take on more technology risk disclosure during an IPO. I quantify the magnitude of disclosed risk and the risk disclosure topics based on a latent Dirichlet allocation (LDA) topic model of IPO prospectus text and find a return-for-risk association between text-based technology risk disclosure and underpricing. The study also finds evidence that owning granted patents is associated with a lower return-for-risk association, suggesting that intellectual property allows the disclosure of risk without losing the competitive advantage. I open-source the code for quantifying risk disclosure as RiskyData-LDA on GitHub.In summary, this dissertation develops theory and finds evidence across three essays to argue that leveraging new technologies requires deliberate strategic planning to manage potential downsides of new technologies, such as the opportunity costs of automation, coordination costs, and costs associated with raising capital. The results suggest three mitigating solutions: dynamic routines to balance learning and automation, a Data Clan to improve coordination, and disclosure through patents to reduce underpricing.

EPFL2022

(Geometry Aware) Deep Learning-based Omnidirectional Image Compression

Yamin Sepehri

Omnidirectional images are the spherical visual signals that provide a wide, 360◦, view of a scene from a specific position. Such images are becoming increasingly popular in fields like virtual reality and robotics. Compared to conventional 2D images, the storage and badwidth requirements of omnidirectional signals are much higher, due to the specific nature of them. Thus, there is a need for image compression schemes to reduce the dedicated storage space of omnidirectional images. Image compression algorithms can be broadly classified into two groups: lossless and lossy. Lossless schemes are able to reconstruct the exact original data but they cannot reduce the size beyond a specific criteria. Lossy methods are generally better solutions if they do not add a high visual distortion to the reconstructed image, as long as they provide a decent compression rate. If a planar, lossy image compression scheme is applied on omnidirectional images, some problems show up. It is possible to apply a planar compression scheme on a projected version of a 360◦image; however, in these projection schemes (such as equirectangular projection) the sampling rate is different in the poles and the center. Consequently, the filters of the planar compression schemes that do not consider this difference ends in suboptimal result and distortions in the reconstructed images. Recently, with the success of deep neural networks in many image processing tasks, researchers began to use them for the image compression as well. In this study, we propose a deep learning-based method for the compression of omnidirectional images by combining some state of the art approaches from the deep learning-based image compression schemes and some special convolutional layers that take into account the geometry of the omnidirectional image. In comparison to the available methods, it is the first method that can be applied directly on the equirectangularly projected version of omnidirectional images and considers the geometry in the scheme and the layers themselves. To propose this method, different geometry-aware convolutional layers have been tried. We exploited various methods of downsampling and upsampling, such as spherical pooling layers, strided or transposed convolutions, bilinear interpolation, and pixel shuffle. In the end, a method is proposed that benefits from specific spherical convolutional layers which contain sampling methods considering the geometry of omnidirectional images. The sampling positions differ in the different heights of the image based on the nature of the projected omnidirectional image. Additionally, as it benefits from an iterative training method that calculates the residual between the output and input and feeds it again to the network as input of the next iteration, it can provide different compression rates with just one pass of training. Finally, it benefits from a novel method of patching that is well-aligned with the spherical convolution layers and helps the method to run efficiently without a need for a high computational power. The model was compared with a similar architecture without spherical convolutions and spherical patching and showed some improvements. The architecture has been optimized and improved and it has the potential to compete with popular image compression schemes such as JPEG especially in terms of reconstructing the colors.

2020

Performance prediction and race detection in message-passing parallel applications

Basile Schäli

The combination of low cost clusters and multicore processors lowers the barrier for accessing massive amounts of computing power. As computational sciences advance, the use of in silico simulations to complement in vivo experiments promises parallel programming a bright future in multiple scientific fields. It is therefore increasingly important to develop tools helping developers to write efficient and bug-free parallel applications. This thesis focuses on performance prediction and advanced testing tools for distributed memory message-passing parallel applications. The tools have been implemented within the Dynamic Parallel Schedules (DPS) parallelization framework. They have also been partly adapted to applications written using the Message Passing Interface (MPI) standard. The first part presents a parallel application simulator which has been integrated into the DPS framework. We identified a small set of processing and networking parameters that characterize the hardware platform on which the application is running. After parameterizing the hardware platform, the running time of parallel applications can be predicted using direct execution without requiring any change to the application source code. We propose a partial direct execution technique that reduces the execution time and memory consumption of the simulation. Using partial direct execution, the simulation is no longer tied to the platform to be simulated. Simulations may thus run on a desktop computer rather than on the target parallel machine. The proposed parameterization of the application and of the hardware properties enable using the simulator to study the sensitivity of a parallel application to various operating conditions such as the data subdivision granularity, the adopted parallelization strategy and the underlying hardware platform properties. The proposed simulator helps developers identifying the factors having the largest impact on their application's performance, and determining the most suitable cluster hardware configuration. Speed should not come at the expense of correctness. Since improving parallelization efficiency often requires loosening synchronizations or implementing more complex communication patterns, developers need to ensure that their changes do not introduce potential message races or deadlocks. Deadlocks and message races are common sources of problems in parallel applications and stem from the fact that the delivery of messages from different sources is not deterministically ordered. This non-determinism makes such synchronization errors hard to reproduce and debug. The second part of the thesis presents methods for uncovering potential deadlocks and message races by taking advantage of the flow graph structures and checkpointing capabilities of DPS. We developed a debugger for DPS applications that displays an instantaneous graphical view of the global computation state and is able to control the ordering of message delivery in order to explicitly test specific orderings. The number of possible orderings explodes when the number of messages sent by the application increases. Manual testing can only cover a tiny fraction of possible executions. Therefore, we use the simulator's ability to control the execution of a parallel application, in order to automatically detect deadlocks and message races. A first method for reducing the number of orderings to be tested relies on a partial-order reduction of the search space and on the decomposition of the application execution into independently testable subparts. This method relies on a static analysis of an execution trace of the application, and can therefore only be applied to parallel applications that produce a fixed set of messages, i.e. applications producing the same messages for all delivery orderings. In order to overcome this limitation, we propose an approach relying on the dynamic construction of a state graph expressing possible executions. Both methods reduce the testing costs by several orders of magnitude, and can be combined to further improve the results. Nevertheless, testing durations may remain prohibitive for longer running applications. We therefore also define algorithms generating subsets of possible orderings that are likely to reveal erroneous executions. In the recent years, the MPI standard has emerged as the de facto standard for writing message-passing parallel applications. The final part of this thesis therefore focuses on adapting the aforementioned parallel application testing concepts to MPI applications. We first describe the extension of our work on visualizing the execution of parallel applications. We then discuss the limits and the benefits of using partial-order execution graphs to describe MPI application executions, and show that our dynamic message-passing state graph construction approach can be successfully applied.

EPFL2009