Related papers: Understanding Persistent-Memory Related Issues in …

Malicious software, or malware, presents a continuously evolving challenge in computer security. These embedded snippets of code in the form of malicious files or hidden within legitimate files cause a major risk to systems with their…

Robust qubit memory is essential for quantum computing, both for near-term devices operating without error correction, and for the long-term goal of a fault-tolerant processor. We directly measure the memory error $\epsilon_m$ for a…

A compiler bug arises if the behaviour of a compiled concurrent program, as allowed by its architecture memory model, is not a behaviour permitted by the source program under its source model. One might reasonably think that most compiler…

Region-based memory management (RBMM) is a form of compile time memory management, well-known from the functional programming world. In this paper we describe our work on implementing RBMM for the logic programming language Mercury. One…

Processing-in-memory (PIM) architectures bring computation closer to data, reducing the processor-memory transfer bottleneck in traditional processor-centric designs. Novel hardware solutions, such as UPMEM's in-memory processing…

Broadband quantum memory is critical to enabling the operation of emerging photonic quantum technology at high speeds. Here we review a central challenge to achieving broadband quantum memory in atomic ensembles -- what we call the…

With the urgent need to secure supply chains among Open Source libraries, attention has focused on mitigating vulnerabilities detected in these libraries. Although awareness has improved recently, most studies still report delays in the…

Applying machine learning to biological sequences - DNA, RNA and protein - has enormous potential to advance human health, environmental sustainability, and fundamental biological understanding. However, many existing machine learning…

Cryptographic algorithms such as AES-128 and SHA-256 are fundamental to ensuring data security and integrity. Although these algorithms are computationally efficient, their performance is often constrained by the processor-centric…

Dynamic analysis based on the full-system emulator QEMU is widely used for various purposes. However, it is challenging to run firmware images of embedded devices in QEMU, especially the process to boot the Linux kernel (we call this…

Continual Learning (CL) is a field dedicated to devise algorithms able to achieve lifelong learning. Overcoming the knowledge disruption of previously acquired concepts, a drawback affecting deep learning models and that goes by the name of…

Persistence of updates to remote byte-addressable persistent memory (PM), using RDMA operations (RDMA updates), is a poorly understood subject. Visibility of RDMA updates on the remote server is not the same as persistence of those updates.…

Poor DRAM technology scaling over the course of many years has caused DRAM-based main memory to increasingly become a larger system bottleneck. A major reason for the bottleneck is that data stored within DRAM must be moved across a…

Continuous-time stochastic processes pervade everyday experience, and the simulation of models of these processes is of great utility. Classical models of systems operating in continuous-time must typically track an unbounded amount of…

Parallel programmers face the often irreconcilable goals of programmability and performance. HPC systems use distributed memory for scalability, thereby sacrificing the programmability advantages of shared memory programming models.…

Ideally the variability of a product line is represented completely and correctly by its variability model. However, in practice additional variability is often represented on the level of the build system or in the code. Such a situation…

We consider the problem of predictive monitoring (PM), i.e., predicting at runtime the satisfaction of a desired property from the current system's state. Due to its relevance for runtime safety assurance and online control, PM methods need…

Multiple Kernel Learning, or MKL, extends (kernelized) SVM by attempting to learn not only a classifier/regressor but also the best kernel for the training task, usually from a combination of existing kernel functions. Most MKL methods seek…

Kubernetes offers a powerful orchestration platform for machine learning training, but memory management can be challenging due to specialized needs and resource constraints. This paper outlines how Kubernetes handles memory requests,…

In recent years, process mining emerged as a proven technology to analyze and improve operational processes. An expanding range of organizations using process mining in their daily operation brings a broader spectrum of processes to be…