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Benefiting from the advancement of hardware accelerators such as GPUs, deep neural networks and scientific computing applications can achieve superior performance. Recently, the computing capacity of emerging hardware accelerators has increased rapidly, while memory bandwidth has not kept pace with this growth. This disparity exacerbates the gap between computing and memory, leading to inefficiencies on conventional algorithms, as they're likely to be converted from compute-bound to memory-bound. Symmetric eigenvalue decomposition (EVD), a critical operation in various research domains including scientific computing, deep learning training, and inference algorithms, exhibits suboptimal performance due to achieving less than 3\% hardware computing utilization on the H100 GPU. In this paper, we analyze the features of emerging hardware accelerators to identify the bottlenecks inherent in conventional EVD algorithms. To improve EVD performance, we propose several algorithmic optimizations aimed at solving the memory-bound problem and providing a better utilization of the rich computing capacity and parallelism on the emerging hardware accelerators. Experimentally, our proposed method demonstrates significant speedups on tridiagonalization, which is the main workload that takes over 90\% elapsed time of EVD, compared to the SOTA cuSOLVER tridiagonalization, achieving up to 10.1x, 7.5x, and 2.3x improvements on H100, A100, and RTX 4090 GPUs, respectively. And the end-to-end the performance of EVD solver is also up to 4.1x faster than cuSOVLER.

We survey the large language model (LLM) serving area to understand the intricate dynamics between cost-efficiency and accuracy, which is magnified by the growing need for longer contextual understanding when deploying models at a massive scale. Our findings reveal that works in this space optimize along three distinct but conflicting goals: improving serving context length (C), improving serving accuracy (A), and improving serving performance (P). Drawing inspiration from the CAP theorem in databases, we propose a CAP principle for LLM serving, which suggests that any optimization can improve at most two of these three goals simultaneously. Our survey categorizes existing works within this framework. We find the definition and continuity of user-perceived measurement metrics are crucial in determining whether a goal has been met, akin to prior CAP databases in the wild. We recognize the CAP principle for LLM serving as a guiding principle, rather than a formal theorem, to inform designers of the inherent and dynamic trade-offs in serving models. As serving accuracy and performance have been extensively studied, this survey focuses on works that extend serving context length and address the resulting challenges.

Large Language Models (LLMs) such as GPTs and LLaMa have ushered in a revolution in machine intelligence, owing to their exceptional capabilities in a wide range of machine learning tasks. However, the transition of LLMs from data centers to edge devices presents a set of challenges and opportunities. While this shift can enhance privacy and availability, it is hampered by the enormous parameter sizes of these models, leading to impractical runtime costs. In light of these considerations, we introduce EdgeMoE, the first on-device inference engine tailored for mixture-of-expert (MoE) LLMs, a popular variant of sparse LLMs that exhibit nearly constant computational complexity as their parameter size scales. EdgeMoE achieves both memory and computational efficiency by strategically partitioning the model across the storage hierarchy. Specifically, non-expert weights are stored in the device's memory, while expert weights are kept in external storage and are fetched into memory only when they are activated. This design is underpinned by a crucial insight that expert weights, though voluminous, are infrequently accessed due to sparse activation patterns. To further mitigate the overhead associated with expert I/O swapping, EdgeMoE incorporates two innovative techniques: (1) Expert-wise bitwidth adaptation: This method reduces the size of expert weights with an acceptable level of accuracy loss. (2) Expert management: It predicts the experts that will be activated in advance and preloads them into the compute-I/O pipeline, thus further optimizing the process. In empirical evaluations conducted on well-established MoE LLMs and various edge devices, EdgeMoE demonstrates substantial memory savings and performance improvements when compared to competitive baseline solutions.

Natural Language Processing (NLP) inference is seeing increasing adoption by mobile applications, where on-device inference is desirable for crucially preserving user data privacy and avoiding network roundtrips. Yet, the unprecedented size of an NLP model stresses both latency and memory, creating a tension between the two key resources of a mobile device. To meet a target latency, holding the whole model in memory launches execution as soon as possible but increases one app's memory footprints by several times, limiting its benefits to only a few inferences before being recycled by mobile memory management. On the other hand, loading the model from storage on demand incurs IO as long as a few seconds, far exceeding the delay range satisfying to a user; pipelining layerwise model loading and execution does not hide IO either, due to the high skewness between IO and computation delays. To this end, we propose Speedy Transformer Inference (STI). Built on the key idea of maximizing IO/compute resource utilization on the most important parts of a model, STI reconciles the latency v.s. memory tension via two novel techniques. First, model sharding. STI manages model parameters as independently tunable shards, and profiles their importance to accuracy. Second, elastic pipeline planning with a preload buffer. STI instantiates an IO/compute pipeline and uses a small buffer for preload shards to bootstrap execution without stalling at early stages; it judiciously selects, tunes, and assembles shards per their importance for resource-elastic execution, maximizing inference accuracy. Atop two commodity SoCs, we build STI and evaluate it against a wide range of NLP tasks, under a practical range of target latencies, and on both CPU and GPU. We demonstrate that STI delivers high accuracies with 1-2 orders of magnitude lower memory, outperforming competitive baselines., Comment: ASPLOS'23

A TrustZone TEE often invokes an external filesystem. While filedata can be encrypted, the revealed file activities can leak secrets. To hide the file activities from the filesystem and its OS, we propose Enigma, a deception-based defense injecting sybil file activities as the cover of the actual file activities. Enigma contributes three new designs. (1) To make the deception credible, the TEE generates sybil calls by replaying file calls from the TEE code under protection. (2) To make sybil activities cheap, the TEE requests the OS to run K filesystem images simultaneously. Concealing the disk, the TEE backs only one image with the actual disk while backing other images by only storing their metadata. (3) To protect filesystem image identities, the TEE shuffles the images frequently, preventing the OS from observing any image for long. Enigma works with unmodified filesystems shipped withLinux. On a low-cost Arm SoC with EXT4 and F2FS, our system can concurrently run as many as 50 filesystem images with 1% of disk overhead per additional image. Compared to common obfuscation for hiding addresses in a flat space, Enigma hides file activities with richer semantics. Its cost is lower by one order of magnitude while achieving the same level of probabilistic security guarantees., Comment: Under submission

While TrustZone can isolate IO hardware, it lacks drivers for modern IO devices. Rather than porting drivers, we propose a novel approach to deriving minimum viable drivers: developers exercise a full driver and record the driver/device interactions; the processed recordings, dubbed driverlets, are replayed in the TEE at run time to access IO devices. Driverlets address two key challenges: correctness and expressiveness, for which they build on a key construct called interaction template. The interaction template ensures faithful reproduction of recorded IO jobs (albeit on new IO data); it accepts dynamic input values; it tolerates nondeterministic device behaviors. We demonstrate driverlets on a series of sophisticated devices, making them accessible to TrustZone for the first time to our knowledge. Our experiments show that driverlets are secure, easy to build, and incur acceptable overhead (1.4x -2.7x compared to native drivers). Driverlets fill a critical gap in the TrustZone TEE, realizing its long-promised vision of secure IO., Comment: Eurosys 2022

Smart devices produce security-sensitive data and keep them in on-device storage for persistence. The current storage stack on smart devices, however, offers weak security guarantees: not only because the stack depends on a vulnerable commodity OS, but also because smart device deployment is known weak on security measures. To safeguard such data on smart devices, we present a novel storage stack architecture that i) protects file data in a trusted execution environment (TEE); ii) outsources file system logic and metadata out of TEE; iii) running a metadata-only file system replica in the cloud for continuously verifying the on-device file system behaviors. To realize the architecture, we build Overwatch, aTrustZone-based storage stack. Overwatch addresses unique challenges including discerning metadata at fine grains, hiding network delays, and coping with cloud disconnection. On a suite of three real-world applications, Overwatch shows moderate security overheads.

By means of high-resolution electron energy loss spectroscopy, we investigate the low-energy excitation spectrum of transition-metal monopnictides hosting Weyl fermions. We observe gapped plasmonic modes in (001)-oriented surfaces of single crystals of NbAs and TaAs at 66 and 68 meV, respectively. Our findings are consistent with theory and we estimate an effective Coulomb interaction strength $\alpha_{\rm eff}\approx0.41$ for both samples. We also demonstrate that the modification of the surface of transition-metal monopnictides by the adsorption of chemical species (in our case, oxygen and hydrocarbon fragments) changes the frequency of the plasmonic excitations, with a subsequent modification of the effective interaction strength in the 0.30-0.48 range. The remarkable dependence of plasmonic features on the presence of adsorbates paves the way for plasmonic sensors based on Weyl semimetals operating in the mid-infrared., Comment: 7 pages, 3 figures

Smart devices see a large number of ephemeral tasks driven by background activities. In order to execute such a task, the OS kernel wakes up the platform beforehand and puts it back to sleep afterwards. In doing so, the kernel operates various IO devices and orchestrates their power state transitions. Such kernel executions are inefficient as they mismatch typical CPU hardware. They are better off running on a low-power, microcontroller-like core, i.e., peripheral core, relieving CPU from the inefficiency. We therefore present a new OS structure, in which a lightweight virtual executor called transkernel offloads specific phases from a monolithic kernel. The transkernel translates stateful kernel execution through cross-ISA, dynamic binary translation (DBT); it emulates a small set of stateless kernel services behind a narrow, stable binary interface; it specializes for hot paths; it exploits ISA similarities for lowering DBT cost. Through an ARM-based prototype, we demonstrate transkernel's feasibility and benefit. We show that while cross-ISA DBT is typically used under the assumption of efficiency loss, it can enable efficiency gain, even on off-the-shelf hardware., Comment: The camera-ready version of this paper, will appear at USENIX ATC'19

Magnetism in Weyl semimetals is desired to investigate the interaction between the magnetic moments and Weyl fermions, e.g. to explore anomalous quantum Hall phenomena. Here we demonstrate that proton irradiation is an effective tool to induce ferromagnetism in the Weyl semimetal TaAs. The intrinsic magnetism is observed with a transition temperature above room temperature. The magnetic moments from d states are found to be localized around Ta atoms. Further, the first-principles calculations indicate that the d states localized on the nearest-neighbor Ta atoms of As vacancy sites are responsible for the observed magnetic moments and the long-ranged magnetic order. The results show the feasibility of inducing ferromagnetism in Weyl semimetals so that they may facilitate the applications of this material in spintronics., Comment: 20 pages, 6 figures

NbTe2 crystal is quasi-2D layered semimetal with charge density wave ground state showing a distorted-1T structure at room temperature. Here we report the anisotropic magneto-transport properties of NbTe2. An anomalous linear magnetoresistance up to 30% at 3 K in 9 T was observed, which can be well explained by quantum linear magnetoresistance model. Our results reveal that a large quasi-2D Fermi surface and small Fermi pockets with linearly dispersive bands coexist in NbTe2. The comparison with the isostructural material TaTe2 provides more information about the electronic structure evolution with charge density wave transitions in NbTe2 and TaTe2., Comment: 5 pages, 4 figures