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Dynamic analysis, through rehosting, is an important capability for security assessment in embedded systems software. Existing rehosting techniques aim to provide high-fidelity execution by accurately emulating hardware and peripheral…

The Memory stress (Mess) framework provides a unified view of the memory system benchmarking, simulation and application profiling. The Mess benchmark provides a holistic and detailed memory system characterization. It is based on hundreds…

Timing-based side and covert channels in processor caches continue to be a threat to modern computers. This work shows for the first time a systematic, large-scale analysis of Arm devices and the detailed results of attacks the processors…

Recent efforts to design and develop Cloud technologies focus on defining novel methods, policies and mechanisms for efficiently managing Cloud infrastructures. One key challenge potential Cloud customers have before renting resources is to…

The emergence of Big Data in recent years has resulted in a growing need for efficient data processing solutions. While infrastructures with sufficient compute power are available, the I/O bottleneck remains. The Linux page cache is an…

As models become larger, ML accelerators are a scarce resource whose performance must be continually optimized to improve efficiency. Existing performance analysis tools are coarse grained, and fail to capture model performance at the…

The paper describes a framework for multi-function system testing. Multi-function system testing is considered as fusion (or revelation) of clique-like structures. The following sets are considered: (i) subsystems (system parts or units /…

Computing-in-Memory (CiM) architectures aim to reduce costly data transfers by performing arithmetic and logic operations in memory and hence relieve the pressure due to the memory wall. However, determining whether a given workload can…

We present and formalize a general approach for profiling workload by leveraging only a priori available static metadata to supply appropriate resource needs. Understanding the requirements and characteristics of a workload's runtime is…

RAPID-LLM is a unified performance modeling framework for large language model (LLM) training and inference on GPU clusters. It couples a DeepFlow-based frontend that generates hardware-aware, operator-level Chakra execution traces from an…

The popularity and wide adoption of JavaScript both at the client and server side makes its code analysis more important than ever before. Most of the algorithms for vulnerability analysis, coding issue detection, or type inference rely on…

We propose a memory-model-aware static program analysis method for accurately analyzing the behavior of concurrent software running on processors with weak consistency models such as x86-TSO, SPARC-PSO, and SPARC-RMO. At the center of our…

Memory profiling captures programs' dynamic memory behavior, assisting programmers in debugging, tuning, and enabling advanced compiler optimizations like speculation-based automatic parallelization. As each use case demands its unique…

The burgeoning RISC-V ecosystem necessitates efficient verification methodologies for complex processors. Traditional approaches often struggle to concurrently evaluate functional correctness and performance, or balance simulation speed…

Algorithms for scheduling structured parallel computations have been widely studied in the literature. For some time now, Work Stealing is one of the most popular for scheduling such computations, and its performance has been studied in…

Applications' performance is influenced by the mapping of processes to computing nodes, the frequency and volume of exchanges among processing elements, the network capacity, and the routing protocol. A poor mapping of application processes…

This work elaborates on a High performance computing (HPC) architecture based on Simple Linux Utility for Resource Management (SLURM) [1] for deploying heterogeneous Large Language Models (LLMs) into a scalable inference engine. Dynamic…

To efficiently support Large Language Models (LLMs), modern GPGPU architectures have introduced new features and programming paradigms, such as warp specialization. These features enable temporal overlap between the producer and consumer,…

The growing demands in the training and inference of Large Language Models (LLMs) are accelerating the adoption of scale-up systems that extend server shared memory through the use of Compute Express Link (CXL)-based load/store…

While discrete-event simulators are essential tools for architecture research, design, and development, their practicality is limited by an extremely long time-to-solution for realistic applications under investigation. This work describes…