26,098 papers · page 159 of 1,305
This paper introduces lazification, a code transformation technique that replaces strict with lazy evaluation of function parameters whenever such modification is deemed profitable. The transformation is designed for an imperative, low-level program representation. It involves a …
Just-in-Time (JIT) compilers are ubiquitous in modern computing systems and are used in a wide variety of software. Dynamic code generation bugs, where the JIT compiler silently emits incorrect code, can result in exploitable vulnerabilities. They, therefore, pose serious securit…
Modern mobile applications have grown rapidly in binary size, which restricts user growth and hinders updates for existing users. Thus, reducing the binary size is important for application developers. Recent studies have shown the possibility of using link-time code size optimiz…
Machine learning (ML) models keep getting larger and more complex. Whereas before models used to be represented by static data-flow graphs, they are now implemented via arbitrary Python code. Eager-mode frameworks, such as PyTorch, are now the standard for developing new ML model…
Dedicated tensor accelerators demonstrate the importance of linear algebra in modern applications. Such accelerators have the potential for impressive performance gains, but require programmers to rewrite code using vendor APIs - a barrier to wider scale adoption. Recent work ove…
Various kinds of applications take advantage of GPUs through automation tools that attempt to automatically exploit the available performance of the GPU's parallel architecture. Directive-based programming models, such as OpenACC, are one such method that easily enables parallel …
Loop interchange is an important code optimization that improves data locality and extracts parallelism. While previous research in compilers has tried to automate the selection of which loops to interchange, existing methods have an important limitation. They use less precise ma…
Advances in computational capabilities and large volumes of experimental data have established computer simulations of brain tissue models as an important pillar in modern neuroscience. Alongside, a variety of domain specific languages (DSLs) have been developed to succinctly exp…
High-performance compilers play an important role in improving the run-time performance of a program, and it is hard and time-consuming to identify the key optimizations implemented in a high-performance compiler with traditional program analysis. In this paper, we propose a hots…
Probabilistic programming languages (PPLs) are essential for reasoning under uncertainty. Even though many real-world probabilistic programs involve discrete distributions, the state-of-the-art PPLs are suboptimal for a large class of tasks dealing with such distributions. In thi…
Optimizing compilers for efficient machine learning are more important than ever due to the rising ubiquity of the application domain in numerous facets of life. Predictive model-guided compiler optimization is sometimes used to derive sequences of loop transformations that optim…
Automatic differentiation (AD) is a central algorithm in deep learning and the emerging field of differentiable programming. However, the performance of AD remains a significant bottleneck in these fields. Training large models requires repeatedly evaluating gradients via AD pote…
We introduce a new scheduling language, based on the formalism of Multi-Dimensional Homomorphisms (MDH). In contrast to existing scheduling languages, our MDH-based language is designed to systematically "de-compose" computations for the memory and core hierarchies of architectur…
Binary code size is a first-class design consideration in many computing domains and a critical factor in many more, but compiler optimizations targeting code size are few and often limited in functionality. When size reduction opportunities are left unexploited, it results in hi…
The simplicity of Python and its rich set of libraries has made it the most popular language for data science. Moreover, the interpreted nature of Python offers an easy debugging experience for the developers. However, it comes with the price of poor performance compared to the c…
Domain-specific languages (DSLs) are able to provide intuitive high-level abstractions that are easy to work with while attaining better performance than general-purpose languages. Yet, implementing new DSLs is a burdensome task. As a result, new DSLs are usually embedded in gene…
The single-thread performance of a processor core is essential even in the multicore era. However, increasing the processing width of a core to improve the single-thread performance leads to a super-linear increase in power consumption. To overcome this power consumption issue, a…
We aim to automate decades of research and experience in register allocation, leveraging machine learning. We tackle this problem by embedding a multi-agent reinforcement learning algorithm within LLVM, training it with the state of the art techniques. We formalize the constraint…
A set of new Hardware Description Languages (HDLs) are emerging to ease hardware design. HDL compilation time is a major bottleneck in the designer’s productivity. Moreover, as the HDLs are developed independently, the possibility to share innovations in compilation technology is…
This paper proposes fast polynomial evaluation methods for correctly rounded elementary functions generated using our RLibm approach. The resulting functions produce correct results for all inputs with multiple representations and rounding modes. Given an oracle, the RLibm approa…