673 papers · page 4 of 34
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…
As quantum computing hardware evolves, it will continue to face four key limitations: low qubit counts, limited connectivity, high error rates, and short coherence times. Quantum compilers play a key role in addressing these issues, reducing the number of qubits needed to perform…
Modern CPUs utilize SIMD vector instructions and hardware extensions to accelerate code with data-level parallelism. This allows for high performance gains in select application domains such as image and signal processing. However, general purpose code often lacks data-level para…
Finding the minimum register bank is an optimization problem related to the synthesis of hardware. Given a program, the problem asks for the minimum number of registers plus their minimum size, in bits, that suffices to compile said program. This problem is NP-complete; hence, us…