Jun Yang

It is our great pleasure to present the IEEE Micro Special Issue on Top Picks From the 2024 Computer Architecture Conferences. This special issue upholds a long-standing tradition within the computer architecture community of recognizing outstanding research contributions. The articles featured herein were selected by the Top Picks Selection Committee for their exceptional novelty and strong potential for long-term impact on the field.

Measurement-based quantum computing (MBQC) is a promising quantum computing paradigm that carries out computation through one-way measurements on entangled photon qubits. Practical photonic hardware first generates a 2D mesh of resource states with each being a small number of entangled photon qubits and then exploits fusion operations to connect resource states to scale up the computation. Given that the fusion operation is highly error-prone, it is important to reduce the number of fusions for an MBQC circuit. In this paper, we propose FCM, a fusion-aware scheme that exploits wire cutting to improve the fidelity of MBQC. By cutting a large MBQC circuit into several smaller subcircuits, FCM effectively reduces the number of fusions in each subcircuit and thus improves the computation fidelity. Given circuit cutting requires classical post-processing to combine the results of subcircuits, FCM strives to achieve the best cutting strategy under different settings. Evaluation of representative benchmarks demonstrates that, when cutting a large circuit to two subcircuits, FCM reduces the maximum number of fusions of all subcircuits by 59.6% on average (up to 69.1%).

Memory safety continues to be a critical challenge in modern computing, with approximately 70% of reported vulnerabilities annually attributed to memory-related issues. Among these issues, Use-After-Free (UAF) vulnerabilities or bugs, where a program accesses memory through a dangling pointer, pose significant threats. Existing UAF detection methods, such as Key-And-Lock (KAL) mechanisms, incur notable performance overhead due to explicitly propagating the key and lock address (metadata). We identify that approximately 67% of KAL’s performance overhead is introduced by this metadata propagation. This paper introduces RTT, which significantly reduces performance overhead by reducing the number of memory accesses related to metadata propagation. RTT achieves an average performance overhead of 170%, substantially lower than existing KAL methods, and incurs only an 8% memory overhead on SPEC CPU 2017. The results of experimental evaluations on real-world UAF bugs further demonstrate that RTT’s UAF bug detection rate is equivalent to other KAL methods.

Multi-GPU systems have emerged as a desirable platform to deliver high computing capabilities and large memory capacity to accommodate large dataset sizes. However, naively employing multi-GPU incurs non-scalable performance. One major reason is that execution efficiency suffers expensive address translations in multi-GPU systems. The data-sharing nature of GPU applications requires page migration between GPUs to mitigate non-uniform memory access overheads. Unfortunately, frequent page migration incurs substantial page table invalidation overheads to ensure translation coherence. A comprehensive investigation of multi-GPU address translation efficiency identifies two significant bottlenecks caused by page table invalidation requests: (i) increased latency for demand TLB miss requests and (ii) increased waiting latency for performing page migrations. Based on observations, we propose IDYLL, which reduces the number of page table invalidations by maintaining an “in-PTE" directory and reduces invalidation latency by batching multiple invalidation requests to exploit spatial locality. We show that IDYLL improves overall performance by 69.9% on average.

Modern processors dynamically adjust clock frequencies and voltages to reduce energy consumption. Recent Intel processors separate the uncore frequency from the core frequency, using Uncore Frequency Scaling (UFS) to adapt the uncore frequency to various workloads. While UFS improves power efficiency, it also introduces security vulnerabilities. In this paper, we study the feasibility of covert channels exploiting UFS. First, we conduct a series of experiments to understand the details of UFS, such as the factors that can cause uncore frequency variations. Then, based on the results, we build the first UFS-based covert channel, UF-variation, which works both across-cores and across-processors. Finally, we analyze the robustness of UF-variation under known defense mechanisms against uncore covert channels, and show that UF-variation remains functional even with those defenses in place.