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- posterAugust 2024
Can AI Coding Assistants Enhance Secure Programming Education? A Comparative Analysis of AI-Based Programming Assistants for Code Optimization
ICER '24: Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 2August 2024, Pages 527–528https://doi.org/10.1145/3632621.3671421Teaching secure programming, including concepts like buffer overflow, is challenging. Advanced AI Coding Assistants aim to help with programming instruction, but it’s unclear how effective they are at promoting secure code. This research compares ...
- research-articleApril 2024
Priority Sampling of Large Language Models for Compilers
EuroMLSys '24: Proceedings of the 4th Workshop on Machine Learning and SystemsApril 2024, Pages 91–97https://doi.org/10.1145/3642970.3655831Large Language Models show great potential in generating and optimizing code. Widely used sampling methods such as Nucleus Sampling increase the diversity of generation but often produce repeated samples for low temperatures and incoherent samples for ...
Concrete Type Inference for Code Optimization using Machine Learning with SMT Solving
Proceedings of the ACM on Programming Languages (PACMPL), Volume 7, Issue OOPSLA2Article No.: 249, Pages 773–800https://doi.org/10.1145/3622825Despite the widespread popularity of dynamically typed languages such as Python, it is well known that they pose significant challenges to code optimization due to the lack of concrete type information. To overcome this limitation, many ahead-of-time ...
- ArticleApril 2023
Equivalence Checking of Code Transformation by Numerical and Symbolic Approaches
Parallel and Distributed Computing, Applications and TechnologiesDec 2022, Pages 373–386https://doi.org/10.1007/978-3-031-29927-8_29AbstractDue to the increasing diversity and complexity of high-performance systems, system-specific optimization is vital to extract the system performance. To ensure that optimization does not degrade code readability and maintainability, user-defined ...
- research-articleJune 2022
A graph neural network-based performance model for deep learning applications
MAPS 2022: Proceedings of the 6th ACM SIGPLAN International Symposium on Machine ProgrammingJune 2022, Pages 11–20https://doi.org/10.1145/3520312.3534863The unprecedented proliferation of machine learning based software brings an ever-increasing need to optimize the implementation of such applications. State-of-the-art compilers for neural networks, such as Halide and TVM, incorporate a machine learning-...
- research-articleFebruary 2020
NeuroVectorizer: end-to-end vectorization with deep reinforcement learning
CGO 2020: Proceedings of the 18th ACM/IEEE International Symposium on Code Generation and OptimizationFebruary 2020, Pages 242–255https://doi.org/10.1145/3368826.3377928One of the key challenges arising when compilers vectorize loops for today’s SIMD-compatible architectures is to decide if vectorization or interleaving is beneficial. Then, the compiler has to determine the number of instructions to pack together and ...
Tiramisu: a polyhedral compiler for expressing fast and portable code
- Riyadh Baghdadi,
- Jessica Ray,
- Malek Ben Romdhane,
- Emanuele Del Sozzo,
- Abdurrahman Akkas,
- Yunming Zhang,
- Patricia Suriana,
- Shoaib Kamil,
- Saman Amarasinghe
CGO 2019: Proceedings of the 2019 IEEE/ACM International Symposium on Code Generation and OptimizationFebruary 2019, Pages 193–205This paper introduces Tiramisu, a polyhedral framework designed to generate high performance code for multiple platforms including multicores, GPUs, and distributed machines. Tiramisu introduces a scheduling language with novel extensions to explicitly ...
- research-articleFebruary 2019
Automatic adaptive approximation for stencil computations
CC 2019: Proceedings of the 28th International Conference on Compiler ConstructionFebruary 2019, Pages 170–181https://doi.org/10.1145/3302516.3307348Approximate computing is necessary to meet deadlines in some compute-intensive applications like simulation. Building them requires a high level of expertise from the application designers as well as a significant development effort. Some application ...
- research-articleOctober 2017
Genetic improvement in code interpreters and compilers
SPLASH Companion 2017: Proceedings Companion of the 2017 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for HumanityOctober 2017, Pages 7–9https://doi.org/10.1145/3135932.3135934Modern compilers provide code optimizations before and during run-time, thus moving required domain knowledge about the compilation process away from the developer and speeding up resulting software. These optimizations are often based on formal proof, ...
- ArticleApril 2015
Software architecture for scalable computing systems with automatic granularity selection of executable code
FRUCT'17: Proceedings of the 17th Conference of Open Innovations Association FRUCTApril 2015, Pages 151–156https://doi.org/10.1109/FRUCT.2015.7117986The problem of developing software architecture and its platform implementation for scalable cloud services is addressed in the paper. New scheme of distributed software developing and executing is presented with argumentation and main principles behind ...
- ArticleSeptember 2013
Early Experiences for Adaptation of Auto-tuning by ppOpen-AT to an Explicit Method
MCSOC '13: Proceedings of the 2013 IEEE 7th International Symposium on Embedded Multicore/Manycore System-on-ChipSeptember 2013, Pages 153–158https://doi.org/10.1109/MCSoC.2013.15We present a code optimization technique by adapting an auto-tuning (AT) function to an explicit method with the static code generator FIBER. The AT function is evaluated with current multicore processors to match situations with high-thread parallelism ...
- ArticleMay 2012
Improving High-Performance Sparse Libraries Using Compiler-Assisted Specialization: A PETSc Case Study
IPDPSW '12: Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD ForumMay 2012, Pages 487–496https://doi.org/10.1109/IPDPSW.2012.63Scientific libraries are written in a general way in anticipation of a variety of use cases that reduce optimization opportunities. Significant performance gains can be achieved by specializing library code to its execution context: the application in ...
- ArticleSeptember 2009
Mapping the FDTD Application to Many-Core Chip Architectures
ICPP '09: Proceedings of the 2009 International Conference on Parallel ProcessingSeptember 2009, Pages 309–316https://doi.org/10.1109/ICPP.2009.44This paper reports a study of mapping the Finite Difference Time Domain (FDTD) application to the IBM Cyclops-64 (C64) many-core chip architecture [1]. C64 is chosen for this study as it represents the current trend in computer architecture to develop a ...