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1. FLoAT : Framework for Workflow Analysis and Transformation

   Jacobson, John; Bentley, Michael; Gopalakrishnan, Ganesh; Ahn, Dong; Lee, Gregory (January 2021, Correctness 2021: Fifth International Workshop on Software Correctness for HPC Applications)

   null; null (Ed.)

   New abstractions and frameworks are born when one creates hard-coded solutions to important tasks, regardless of whether they scale or result in software that can be meaningfully released. This paper describes our experience creating such a light-weight framework out of a previous tool effort FLiT for detecting compiler-induced numerical variability. The resulting framework FLOAT has already helped us better understand and fix performance bugs in FLiT. Our design of FLOAT and the ways in which we anticipate it enabling the adoption and re-purposing of FLiT, though likely not exhaustive, are described. We also express our views on the appropriate scope of such an approach, especially given that variations of compilation, linking, and execution abound, and specializing in that domain may be advantageous in the long-term as opposed to investing in an overly generalized paradigm. 

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2. Techniques for Managing Polyhedral Dataflow Graphs

   https://doi.org/10.1007/978-3-030-99372-6_9  

   Shankar, Ravi; Orenstein, Aaron; Rift, Anna; Popoola, Tobi; Lowe; MacDonald; Yang, Shuai; Mikesell, T. Dylan; Olschanowsky, Catherine (October 2021, Languages and Compilers for Parallel Computing: 34th International Workshop, LCPC 2021)

   Scientific applications, especially legacy applications, contain a wealth of scientific knowledge. As hardware changes, applications need to be ported to new architectures and extended to include scientific advances. As a result, it is common to encounter problems like performance bottlenecks and dead code. A visual representation of the dataflow can help performance experts identify and debug such problems. The Computation API of the sparse polyhedral framework (SPF) provides a single entry point for tools to generate and manipulate polyhedral dataflow graphs, and transform applications. However, when viewing graphs generated for scientific applications there are several barriers. The graphs are large, and manipulating their layout to respect execution order is difficult. This paper presents a case study that uses the Computation API to represent a scientific application, GeoAc, in the SPF. Generated polyhedral dataflow graphs were explored for optimization opportunities and limitations were addressed using several graph simplifications to improve their usability. 

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3. Floating-Point Neural Networks are Provably Robust Universal Approximators

   https://doi.org/10.1007/978-3-031-98679-6_14  

   Hwang, Geonho; Lee, Wonyeol; Park, Yeachan; Park, Sejun; Saad, Feras (January 2025, Springer Nature Switzerland)

   The classical universal approximation (UA) theorem for neural networks establishes mild conditions under which a feedforward neural network can approximate a continuous functionfwith arbitrary accuracy. A recent result shows that neural networks also enjoy a more generalintervaluniversal approximation (IUA) theorem, in the sense that the abstract interpretation semantics of the network using the interval domain can approximate the direct image map off(i.e., the result of applyingfto a set of inputs) with arbitrary accuracy. These theorems, however, rest on the unrealistic assumption that the neural network computes over infinitely precise real numbers, whereas their software implementations in practice compute over finite-precision floating-point numbers. An open question is whether the IUA theorem still holds in the floating-point setting. This paper introduces the first IUA theorem forfloating-pointneural networks that proves their remarkable ability toperfectly capturethe direct image map of any rounded target functionf, showing no limits exist on their expressiveness. Our IUA theorem in the floating-point setting exhibits material differences from the real-valued setting, which reflects the fundamental distinctions between these two computational models. This theorem also implies surprising corollaries, which include (i) the existence ofprovably robustfloating-point neural networks; and (ii) thecomputational completenessof the class of straight-line programs that use only floating-point additions and multiplications for the class of all floating-point programs that halt. 

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4. Fuzzing Configurations of Program Options

   https://doi.org/10.1145/3580597  

   Zhang, Zenong; Klees, George; Wang, Eric; Hicks, Michael; Wei, Shiyi (April 2023, ACM Transactions on Software Engineering and Methodology)

   While many real-world programs are shipped with configurations to enable/disable functionalities, fuzzers have mostly been applied to test single configurations of these programs. In this work, we first conduct an empirical study to understand how program configurations affect fuzzing performance. We find that limiting a campaign to a single configuration can result in failing to cover a significant amount of code. We also observe that different program configurations contribute differing amounts of code coverage, challenging the idea that each one can be efficiently fuzzed individually. Motivated by these two observations, we propose ConfigFuzz , which can fuzz configurations along with normal inputs. ConfigFuzz transforms the target program to encode its program options within part of the fuzzable input, so existing fuzzers’ mutation operators can be reused to fuzz program configurations. We instantiate ConfigFuzz on six configurable, common fuzzing targets, and integrate their executions in FuzzBench. In our evaluation, ConfigFuzz outperforms two baseline fuzzers in four targets, while the results are mixed in the other targets due to program size and configuration space. We also analyze the options fuzzed by ConfigFuzz and how they affect the performance. 

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5. Ergodic LfD: Learning from what to do and what not to do

   Aleksandra Kalinowska, Ahalya Prabhakar (January 2021, IEEE International Conference on Robotics and Automation)

   null (Ed.)

   With growing access to versatile robotics, it is beneficial for end users to be able to teach robots tasks without needing to code a control policy. One possibility is to teach the robot through successful task executions. However, near-optimal demonstrations of a task can be difficult to provide and even successful demonstrations can fail to capture task aspects key to robust skill replication. Here, we propose a learning from demonstration (LfD) approach that enables learning of robust task definitions without the need for near-optimal demonstrations. We present a novel algorithmic framework for learning task specifications based on the ergodic metric—a measure of information content in motion. Moreover, we make use of negative demonstrations— demonstrations of what not to do—and show that they can help compensate for imperfect demonstrations, reduce the number of demonstrations needed, and highlight crucial task elements improving robot performance. In a proof-of-concept example of cart-pole inversion, we show that negative demonstrations alone can be sufficient to successfully learn and recreate a skill. Through a human subject study with 24 participants, we show that consistently more information about a task can be captured from combined positive and negative (posneg) demonstrations than from the same amount of just positive demonstrations. Finally, we demonstrate our learning approach on simulated tasks of target reaching and table cleaning with a 7-DoF Franka arm. Our results point towards a future with robust, data efficient LfD for novice users. 

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