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1. Fast And Automatic Floating Point Error Analysis With CHEF-FP

   https://doi.org/10.1109/IPDPS54959.2023.00105  

   Singh, Garima ; Kundu, Baidyanath ; Menon, Harshitha ; Penev, Alexander ; Lange, David J. ; Vassilev, Vassil ( May 2023 , 2023 IEEE International Parallel and Distributed Processing Symposium)

   As we reach the limit of Moore’s Law, researchers are exploring different paradigms to achieve unprecedented performance. Approximate Computing (AC), which relies on the ability of applications to tolerate some error in the results to trade-off accuracy for performance, has shown significant promise. Despite the success of AC in domains such as Machine Learning, its acceptance in High-Performance Computing (HPC) is limited due to its stringent requirement of accuracy. We need tools and techniques to identify regions of the code that are amenable to approximations and their impact on the application output quality so as to guide developers to employ selective approximation. To this end, we propose CHEF-FP, a flexible, scalable, and easy-to-use source-code transformation tool based on Automatic Differentiation (AD) for analysing approximation errors in HPC applications. CHEF-FP uses Clad, an efficient AD tool built as a plugin to the Clang compiler and based on the LLVM compiler infrastructure, as a backend and utilizes its AD abilities to evaluate approximation errors in C++ code. CHEF-FP works at the source level by injecting error estimation code into the generated adjoints. This enables the error-estimation code to undergo compiler optimizations resulting in improved analysis time and reduced memory usage. We also provide theoretical and architectural augmentations to source code transformation-based AD tools to perform FP error analysis. In this paper, we primarily focus on analyzing errors introduced by mixed-precision AC techniques, the most popular approximate technique in HPC. We also show the applicability of our tool in estimating other kinds of errors by evaluating our tool on codes that use approximate functions. Moreover, we demonstrate the speedups achieved by CHEF-FP during analysis time as compared to the existing state-of-the-art tool as a result of its ability to generate and insert approximation error estimate code directly into the derivative source. The generated code also becomes a candidate for better compiler optimizations contributing to lesser runtime performance overhead. 

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2. Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients

   Moses, William ; Churavy, Valentin ( January 2020 , Advances in neural information processing systems)

   Applying differentiable programming techniques and machine learning algorithms to foreign programs requires developers to either rewrite their code in a machine learning framework, or otherwise provide derivatives of the foreign code. This paper presents Enzyme, a high-performance automatic differentiation (AD) compiler plugin for the LLVM compiler framework capable of synthesizing gradients of statically analyzable programs expressed in the LLVM intermediate representation (IR). Enzyme synthesizes gradients for programs written in any language whose compiler targets LLVM IR including C, C++, Fortran, Julia, Rust, Swift, MLIR, etc., thereby providing native AD capabilities in these languages. Unlike traditional source-to-source and operator-overloading tools, Enzyme performs AD on optimized IR. On a machine-learning focused benchmark suite including Microsoft's ADBench, AD on optimized IR achieves a geometric mean speedup of 4.2 times over AD on IR before optimization allowing Enzyme to achieve state-of-the-art performance. Packaging Enzyme for PyTorch and TensorFlow provides convenient access to gradients of foreign code with state-of-the-art performance, enabling foreign code to be directly incorporated into existing machine learning workflows. 

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3. Non-parametric Models for Long-term Autonomy

   Acshi Haggenmiller ( July 2022 , none)

   In this thesis we propose novel estimation techniques for localization and planning problems, which are key challenges in long-term autonomy. We take inspiration in our methods from non-parametric estimation and use tools such as kernel density estimation, non-linear least-squares optimization, binary masking, and random sampling. We show that these methods, by avoiding explicit parametric models, outperform existing methods that use them. Despite the seeming differences between localization and planning, we demonstrate in this thesis that the problems share core structural similarities. When real or simulation-sampled measurements are expensive, noisy, or high variance, non-parametric estimation techniques give higher-quality results in less time. We first address two localization problems. In order to permit localization with a set of ad hoc-placed radios, we propose an ultra-wideband (UWB) graph realization system to localize the radios. Our system achieves high accuracy and robustness by using kernel density estimation for measurement probability densities, by explicitly modeling antenna delays, and by optimizing this combination with a non-linear least squares formulation. Next, in order to then support robotic navigation, we present a flexible system for simultaneous localization and mapping (SLAM) that combines elements from both traditional dense metric SLAM and topological SLAM, using a binary "masking function" to focus attention. This masking function controls which lidar scans are available for loop closures. We provide several masking functions based on approximate topological class detectors. We then examine planning problems in the final chapter and in the appendix. In order to plan with uncertainty around multiple dynamic agents, we describe Monte-Carlo Policy-Tree Decision Making (MCPTDM), a framework for efficiently computing policies in partially-observable, stochastic, continuous problems. MCPTDM composes a sequence of simpler closed-loop policies and uses marginal action costs and particle repetition to improve cost estimates and sample efficiency by reducing variance. Finally, in the appendix we explore Learned Similarity Monte-Carlo Planning (LSMCP), where we seek to enhance the sample efficiency of partially observable Monte Carlo tree search-based planning by taking advantage of similarities in the final outcomes of similar states and actions. We train a multilayer perceptron to learn a similarity function which we then use to enhance value estimates in the planning. Collectively, we show in this thesis that non-parametric methods promote long-term autonomy by reducing error and increasing robustness across multiple domains. 

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4. DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

   Muller, Raymond ( January 2022 , Workshop on Automotive and Autonomous Vehicle Security (AutoSec))

   With emerging vision-based autonomous driving (AD) systems, it becomes increasingly important to have datasets to evaluate their correct operation and identify potential security flaws. However, when collecting a large amount of data, either human experts manually label potentially hundreds of thousands of image frames or systems use machine learning algorithms to label the data, with the hope that the accuracy is good enough for the application. This can become especially problematic when tracking the context information, such as the location and velocity of surrounding objects, useful to evaluate the correctness and improve stability and robustness of the AD systems. In this paper, we introduce DRIVETRUTH, a data collection framework built on CARLA, an open-source simulator for AD research, which constructs datasets with automatically generated accurate object labels, bounding boxes of objects and their contextual information through accessing simulation state and using semantic LiDAR raycasts. By leveraging the actual state of the simulation and the agents within it, we guarantee complete accuracy in all labels and gathered contextual information. Further, the use of the simulator provides easily collecting data in diverse environmental conditions and agent behaviors, with lighting, weather, and traffic behavior being configurable within the simulation. Through this effort, we provide users a means to extracting actionable simulated data from CARLA to test and explore attacks and defenses for AD systems. 

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5. A domain-specific language for exploratory data visualization

   https://doi.org/10.1145/3278122.3278138  

   Smeltzer, Karl ; Erwig, Martin ( January 2018 , 17th ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences)

   With an ever-growing amount of collected data, the importance of visualization as an analysis component is growing in concert. The creation of good visualizations often doesn't happen in one step but is rather an iterative and exploratory process. However, this process is currently not well supported in most of the available visualization tools and systems. Visualization authors are forced to commit prematurely to particular design aspects of their creations, and when exploring potential variant visualizations, they are forced to adopt ad hoc techniques such as copying code snippets or keeping a collection of separate files. We propose variational visualizations as a model supporting open-ended exploration of the design space of information visualization. Together with that model, we present a prototype implementation in the form of a domain-specific language embedded in Purescript. 

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