26,098 papers · page 214 of 1,305
Term Rewriting Systems (TRSs) are used in compilers to simplify and prove expressions. State-of-the-art TRSs in compilers use a greedy algorithm that applies a set of rewriting rules in a predefined order (where some of the rules are not axiomatic). This leads to a loss of the ab…
Positive user experience of mobile apps demands they not only launch fast and run fluidly, but are also small in order to reduce network bandwidth from regular updates. Conventional optimizations often trade off size regressions for performance wins, making them impractical in th…
Many program analysis techniques depend on capturing the control dependencies of the program. Most existing control dependence algorithms either compute intraprocedural control dependencies only, or they compute control dependence relations that are not precise in general includi…
The introduction of High Bandwidth Memory (HBM) necessitates the use of intelligent software prefetching in irregular applications to utilize the surplus bandwidth. In this work, we propose Software Pre-execution (SPE), a technique that relies on pre-executing a minimal copy of t…
Graphics Processing Units (GPUs) are notoriously hard to optimize for manually. What is needed are good automatic code generators and optimizers. Accelerate, Futhark and Lift demonstrated that a functional approach is well suited for this challenge. Lift, for instance, uses a sys…
Accelerated computing has increased the need to specialize how a program is parallelized depending on the target. Fully exploiting a highly parallel accelerator, such as a GPU, demands more parallelism and sometimes more levels of parallelism than a multicore CPU. OpenMP has a di…
Quantum computing hardware has progressed rapidly. Simultaneously, there has been a proliferation of programming languages and program optimization tools for quantum computing. Existing quantum compilers use intermediate representations (IRs) where quantum programs are described …
Auto-tuning DL compilers are gaining ground as an optimizing back-end for DL frameworks. While existing work can generate deep learning models that exceed the performance of hand-tuned libraries, they still suffer from prohibitively long auto-tuning time due to repeated hardware …
We present an approach to integrating state-of-art bottom-up logic programming within the Rust ecosystem, demonstrating it with Ascent, an extension of Datalog that performs well against comparable systems. Rust’s powerful macro system permits Ascent to be compiled uniformly with…
This paper presents graph transformation algorithms for register-pressure-aware instruction scheduling. The proposed transformations add edges to the data dependence graph (DDG) to eliminate solutions that are either redundant or sub-optimal. Register-pressure-aware instruction s…
Reinforcement learning (RL) is emerging as a powerful technique for solving complex code optimization tasks with an ample search space. While promising, existing solutions require a painstaking manual process to tune the right task-specific RL architecture, for which compiler dev…
Training models with massive inputs is a significant challenge in the development of Deep Learning pipelines to process very large digital image datasets as required by Whole Slide Imaging (WSI) in computational pathology and analysis of brain fMRI images in computational neurosc…
Cache side-channel attacks pose real threats to computer system security. Prior work called Cloak leverages commodity hardware transactional memory (HTM) to protect sensitive data and code from cache side-channel attacks. However, Cloak requires tedious and error-prone manual mod…
Despite ceaseless efforts, extremely large and complex optimization space makes even the state-of-the-art compilers fail in delivering the most performant setting that can fully utilize the underlying hardware. Although this inefficiency suggests opportunity for tuning, it has be…
Automatic parallel code generation from high-level abstractions such as those manipulated by artificial intelligence and deep learning (AI/DL) frameworks heavily rely on compiler techniques for automatic parallelization and optimization. Many recent advances rely on the polyhedra…
Static Single Assignment (SSA) is the workhorse of modern optimizing compilers for imperative programming languages. However, functional languages have been slow to adopt SSA and prefer to use intermediate representations based on minimal lambda calculi due to SSA’s inability to …
Domain-Specific Languages (DSLs) provide the optimum balance between generalization and specialization that is crucial to getting the best performance for a particular domain. DSLs like Halide and GraphIt and their rich scheduling languages allow users to generate an implementati…
Interest in applying Artificial Intelligence (AI) techniques to compiler optimizations is increasing rapidly, but compiler research has a high entry barrier. Unlike in other domains, compiler and AI researchers do not have access to the datasets and frameworks that enable fast it…
In a super-scalar architecture, the scheduler dynamically assigns micro-operations $( \mu$ OPs) to execution ports. The port mapping of an architecture describes how an instruction decomposes into $\mu$ OPs and lists for each $\mu$ OP the set of ports it can be mapped to. It is u…
This paper shows how to extend sparse tensor algebra compilers to support lossless compression techniques, including variants of run-length encoding and Lempel-Ziv compression. We develop new abstractions to represent losslessly compressed data as a generalized form of sparse ten…