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1. 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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2. 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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3. DRIVETRUTH: Automated Autonomous Driving Dataset Generation for Security Applications

   https://doi.org/10.14722/autosec.2022.23032  

   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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4. A general purpose tool-set for representing data relationships: Converting data into knowledge

   https://doi.org/10.1109/nysds.2016.7747809  

   Stillerman, Joshua; Fredian, Thomas; Greenwald, Martin; Wright, John (August 2016, IEEE 2016 New York Scientific Data Summit (NYSDS))

   Rich metadata is required to find and understand the recorded measurements from modern experiments with their immense and complex data stores. Systems to store and manage these metadata have improved over time, but in most cases are ad-hoc collections of data relationships, often represented in domain or site specific application code. We are developing a general set of tools to store, manage, and retrieve datarelationship metadata. These tools will be agnostic to the underlying data storage mechanisms, and to the data stored in them, making the system applicable across a wide range of science domains. 

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5. MATILDA.FT: A mesoscale simulation package for inhomogeneous soft matter

   https://doi.org/10.1063/5.0145006  

   Jedlinska, Zuzanna M.; Tabedzki, Christian; Gillespie, Colin; Hess, Nathaniel; Yang, Anita; Riggleman, Robert A. (July 2023, The Journal of Chemical Physics)

   In this paper, we announce the public release of a massively parallel, graphics processing unit (GPU)-accelerated software, which is the first to combine both coarse-grained particle simulations and field-theoretic simulations in one simulation package. MATILDA.FT (Mesoscale, Accelerated, Theoretically Informed, Langevin, Dissipative particle dynamics, and Field Theory) was designed from the ground-up to run on CUDA-enabled GPUs with Thrust library acceleration, enabling it to harness the possibility of massive parallelism to efficiently simulate systems on a mesoscopic scale. It has been used to model a variety of systems, from polymer solutions and nanoparticle-polymer interfaces to coarse-grained peptide models and liquid crystals. MATILDA.FT is written in CUDA/C++ and is object oriented, making its source-code easy to understand and extend. Here, we present an overview of the currently available features, and the logic of parallel algorithms and methods. We provide the necessary theoretical background and present examples of systems simulated using MATILDA.FT as the simulation engine. The source code, along with the documentation, additional tools, and examples, can be found on the GitHub MATILDA.FT repository. 

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