673 papers · page 5 of 34
It has been widely observed that data movement is emerging as the primary bottleneck to scalability and energy efficiency in future hardware, especially for applications and algorithms that are not cache-friendly and achieve below 1% of peak performance on today’s systems. The id…
This paper introduces QRANE, a tool that produces the affine intermediate representation (IR) from a quantum program expressed in Quantum Assembly language such as OpenQASM. QRANE finds subsets of quantum gates prescribed by the same operation type and linear relationships, and c…
Developing a bug-free compiler is difficult; modern optimizing compilers are among the most complex software systems humans build. Fuzzing is one way to identify subtle compiler bugs that are hard to find with human-constructed tests. Grammar-based fuzzing, however, requires a gr…
The state-of-the-art in high-performance deep learning today is primarily driven by manually developed libraries optimized and highly tuned by expert programmers using low-level abstractions with significant effort. This effort is often repeated for similar hardware and future on…
Binary-level pointer analysis is critical to binary-level applications such as reverse engineering and binary debloating. In this paper, we propose BinPointer, a new binary-level interprocedural pointer analysis that relies on an offset-sensitive value-tracking analysis to achiev…
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…
Python has become one of the most used and taught languages nowadays. Its expressiveness, cross-compatibility and ease of use have made it popular in areas as diverse as finance, bioinformatics or machine learning. However, Python programs are often significantly slower to execut…
With the emergence of reconfigurable FPGA circuits as a credible alternative to GPUs for HPC acceleration, new compilation paradigms are required to map high-level algorithmic descriptions to a circuit configuration (High-Level Synthesis, HLS). In particular, novel parallelizatio…