736 papers · page 9 of 37
High-level parallel languages (HLPLs) make it easier to write correct parallel programs. Disciplined memory usage in these languages enables new optimizations for hardware bottlenecks, such as cache coherence. In this work, we show how to reduce the costs of cache coherence by in…
Four and a half years after PyTorch 1.0, we announced PyTorch 2.0 at the PyTorch Conference last December. The message was simple – introducing compiled mode, torch.compile(), to the core of PyTorch. This talk shares our 5-year journey of finding the right compiler solutions for …
Software obfuscation techniques can prevent binary diffing techniques from locating vulnerable code by obfuscating the third-party code, to achieve the purpose of protecting embedded device software. With the rapid development of binary diffing techniques, they can achieve more a…
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…
OpenMP is the preferred choice for CPU parallelism in High-Performance-Computing (HPC) applications written in C, C++, or Fortran. As HPC systems became heterogeneous, OpenMP introduced support for accelerator offloading via the target directive. This allowed porting existing (CP…
Dataflow languages have, as well as functional languages, immutable semantics, which is often implemented by copying values. A common compiler optimization known from functional languages involves analyzing which data structures can be modified in-place instead of copying them. T…
Irregularly structured registers are hard to abstract and allocate. Partitioned Boolean quadratic programming (PBQP) is a useful abstraction to represent complex register constraints, even those in highly irregular processors of automated test equipment (ATE) of DRAM memory chips…
Modern compiler optimization pipelines are large and complex, and it is rather cumbersome and error-prone for compiler developers to preserve debugging information across optimization passes. An optimization can add, remove, or reorder code and variables, which makes it difficult…
Despite the benefit of Fully Homomorphic Encryption (FHE) that supports encrypted computation, writing an efficient FHE application is challenging due to magnitude scale management. Each FHE operation increases scales of ciphertext and leaving the scales high harms performance of…
We present automatic horizontal fusion, a novel optimization technique that complements the standard kernel fusion techniques for GPU programs. Unlike the standard fusion, whose goal is to eliminate intermediate data round trips, our horizontal fusion technique aims to increase t…
Modern and emerging architectures demand increasingly complex compiler analyses and transformations. As the emphasis on compiler infrastructure moves beyond support for peephole optimizations and the extraction of instruction-level parallelism, compilers should support custom too…
Recently, natural language (NL)-based program synthesis has drawn increasing interest. Conventional methods that depend on some predefined domain-specific rules suffer from the lack of robustness and generality. Recent efforts on adopting deep learning to map queries to code requ…
Dynamic parallelism on GPUs allows GPU threads to dynamically launch other GPU threads. It is useful in applications with nested parallelism, particularly where the amount of nested parallelism is irregular and cannot be predicted beforehand. However, prior works have shown that …
Floating-point arithmetic is extensively used in scientific and engineering applications to approximate real arithmetic. Unfortunately, floating-point arithmetic is not a sound implementation of real arithmetic, i.e., it may produce different results, does not provide error guara…