736 papers · page 4 of 37
Comprehensive application security can only be ensured if all code that it is going to execute is protected: any unprotected code, either from libraries or the application, becomes a potential attack surface. Compilers contain extensive suites of tools to aid in this, but require…
The Extended Berkeley Packet Filter (eBPF) is a sandboxed virtual machine that runs on operating systems with kernel privileges. Currently, eBPF programs are either translated from a subset of C, called Restricted C, or from bindings available for languages such as Rust or Python…
C++ remains one of the most widely used languages in various computing fields, from embedded programming to high-performance computing. While new features are constantly being added to C++, an important aspect of the language that is often overlooked is its compilation time. Mere…
With the increasing popularity of Serverless computing and Function as a Service---where typical workloads have a short lifetime---the research community is increasingly focusing on startup performance optimization. To reduce the startup time of managed language runtime systems, …
Tensor domain specific languages (DSLs) achieve substantial performance due to high-level compiler optimization and hardware acceleration. However, to achieve such performance for existing applications requires the programmer to manual rewrite their legacy code in evolving Tensor…
With the proliferation of mobile devices in daily life, ensuring the security and performance of these devices has become crucial. On Android, the Java Native Interface (JNI) acts as a bridge, allowing native libraries to directly access Java heap memory via raw pointers, bypassi…
PyPM is a Python-based domain specific language (DSL) for building rewrite-based optimization passes on machine learning computation graphs. Users define individual optimizations by writing (a) patterns that match subgraphs of a computation graph and (b) corresponding rules which…
Memory access instructions with auto-addressing modes are prevalent in various Instruction Set Architectures (ISAs), yet their use in compilers remains limited. Existing methods address code optimization in one of two ways: they either focus on reducing code size, but are constra…
Traditionally, compiler researchers either conduct experiments within an existing production compiler or develop their own prototype compiler; both options come with trade-offs. On one hand, prototyping in a production compiler can be cumbersome, as they are often optimized for p…
In High-performance computing (HPC) fast application execution is the primary objective. HPC software is written in high-performance languages (C/C++, Fortran) and is statically compiled Ahead-of-Time (AOT) using optimizing compilers to generate fast code. AOT compilation optimiz…
Most compilers convert all iterations of a vectorizable loop into vector operations to decrease processing time. This paper proposes Scalar Interpolation, a technique that inserts scalar operations into vectorized loops to increase the utilization of execution units in processors…
Large language models (LLMs) are remarked by their substantial computational requirements. To mitigate the cost, researchers develop specialized CUDA kernels, which often fuse several tensor operations to maximize the utilization of GPUs as much as possible. However, those specia…
This paper introduces a technique for synthesizing hash functions specialized to particular byte formats. This code generation method leverages three prevalent patterns: (i) fixed-length keys, (ii) keys with common subsequences, and (iii) keys ranging on predetermined sequences o…
We introduce Stardust, a compiler from sparse tensor algebra languages to a sparse reconfigurable dataflow architecture via a parallel-patterns programming model. Stardust lets performance engineers specify the placement of data into memories separately from the placement of comp…
A zoned neutral atom architecture achieves exceptional fidelity by segregating the execution spaces of 1- and 2-qubit gates, being a promising candidate for high-accuracy quantum systems. Unfortunately, na'ively applying programs designed for static qubit topologies to zoned arch…
Memory safety violations due to C's undefined behavior, although well researched, still cause security breaches year by year. The most dangerous reported violations are spatial safety violations, where objects are accessed outside of their bounds. A wide variety of spatial safety…
Real-time processing of deep learning models on conventional systems, such as CPUs and GPUs, is highly challenging due to memory bottlenecks. This is exacerbated in Large Language Models (LLMs), where the majority of executions are dominated by General Matrix Multiplication (GEMM…
While the prominent quantum computing architectures are based on superconducting technology, new quantum hardware technologies are emerging, such as Trapped Ions, Neutral Atoms (or FPQAs), Silicon Spin Qubits, etc. This diverse set of technologies presents fundamental trade-offs …
A typical points-to analysis such as Andersen’s or Steensgaard’s may lose precision because it ignores the branching structure of the analyzed program. Moreover, points-to analysis typically focuses on objects only, not considering instructions manipulating primitive values. We a…
Large Language Models (LLMs) have recently emerged as a state-of-the-art learning model with a wide range of applications in diverse computing environments. Among the various computational operations that comprise the LLM, the GEneral Matrix Multiplication (GEMM) operation is the…