Large Language Models (LLMs) with Mixture-of-Expert (MoE) architectures achieve superior model performance with reduced computation costs, but at the cost of high memory capacity and bandwidth requirements. Near-Memory Processing (NMP) accelerators that stack memory directly on the compute through hybrid bonding have demonstrated high bandwidth with high energy efficiency, becoming a promising architecture for MoE models. However, as NMP accelerators comprise distributed memory and computation, how to map the MoE computation directly determines the LLM inference efficiency. Existing parallel mapping strategies, including Tensor Parallelism (TP) and Expert Parallelism (EP), suffer from either high communication costs or unbalanced computation utilization, leading to inferior efficiency. The dynamic routing mechanism of MoE LLMs further aggravates the efficiency challenges. Therefore, in this paper, we propose HD-MoE to automatically optimize the MoE parallel computation across an NMP accelerator. HD-MoE features an offline automatic hybrid parallel mapping algorithm and an online dynamic scheduling strategy to reduce the communication costs while maximizing the computation utilization. With extensive experimental results, we demonstrate that HD-MoE achieves a speedup ranging from 1.1× to 1.8× over TP, 1.1× to 1.5× over EP, and 1.0× to 1.4× over the baseline Hybrid TP-EP with Compute-Balanced parallelism strategies.

Unused definitions are values assigned to variables but not used. Since unused definitions are usually considered redundant code causing no severe consequences except for wasting CPU cycles, system developers usually treat them as mild warnings and simply remove them. In this paper, we reevaluate the effect of unused definitions and discover that some unused definitions could indicate non-trivial bugs like security issues or data corruption, which calls for more attention from developers.

Database-backed web applications persist a large amount of production data and have high requirements for integrity. To protect data integrity against application code bugs and operator mistakes, most RDBMSes allow application developers to specify various types of integrity constraints. Unfortunately, applications (e.g., e-commerce web apps) often do not take full advantage of this capability and miss specifying many database constraints, resulting in many severe consequences, such as crashing the order placement page and corrupting the store inventory data.

Access control is often reported to be "profoundly broken" in real-world practices due to prevalent policy misconfigurations introduced by system administrators (sysadmins). Given the dynamics of resource and data sharing, access control policies need to be continuously updated. Unfortunately, to err is human-sysadmins often make mistakes such as over-granting privileges when changing access control policies. With today's limited tooling support for continuous validation, such mistakes can stay unnoticed for a long time until eventually being exploited by attackers, causing catastrophic security incidents. We present P-DIFF, a practical tool for monitoring access control behavior to help sysadmins early detect unintended access control policy changes and perform postmortem forensic analysis upon security attacks. P-DIFF continuously monitors access logs and infers access control policies from them. To handle the challenge of policy evolution, we devise a novel time-changing decision tree to effectively represent access control policy changes, coupled with a new learning algorithm to infer the tree from access logs. P-DIFF provides sysadmins with the inferred policies and detected changes to assist the following two tasks: (1) validating whether the access control changes are intended or not; (2) pinpointing the historical changes responsible for a given security attack. We evaluate P-DIFF with a variety of datasets collected from five real-world systems, including two from industrial companies. P-DIFF can detect 86%-100% of access control policy changes with an average precision of 89%. For forensic analysis, P-DIFF can pinpoint the root-cause change that permits the target access in 85%-98% of the evaluated cases.