分布式、并行与集群计算

The I/O performance of large-scale HPC applications depends on a complex interplay of access patterns, middleware optimizations, and file system configurations. To systematically explore these effects without repeatedly rerunning full…

Mixture-of-Experts (MoE) architectures enable language models to achieve unprecedented scale via sparse activation. However, their inference performance is often limited by data movement bottlenecks. Two coupled challenges exacerbate this…

The emergence of Multi-Instance GPU (MIG) technology enables us to run smaller machine learning models on partitions of a GPU rather than the entire device, thus improving utilization and reducing energy consumption, albeit with potential…

Heterogeneous prefill-decode (PD) inference is now in production: prefill on cost-efficient or supply-available accelerators, decode on bandwidth-strong ones, and KV state crossing mixed interconnects in mixed numerical formats. Each…

Counting the orbits of graphlets in a network is a vital tool for understanding the structural roles of vertices in various graph analytics tasks. While existing algorithms efficiently compute orbits of induced graphlets, many real-world…

Multimodal model inference creates substantial energy demand with growing performance requirements. Within GPUs, power is autonomously managed by an on-board power management unit (PMU), which makes frequency boosting/throttling decisions.…

Fog computing utilizes proximal computational resources for sensor data processing and actuation, and addresses the latency, network load, and privacy issues of cloud-centric Internet of Things. On the other hand, Large Language Models…

LLM serving is increasingly dominated by long and dynamic decode workloads from agents, reasoning models, and extended conversations. When bursty long-context demand exceeds deployed capacity, existing serving systems typically scale out by…

Based on the Backblaze hard disk drive (HDD) dataset, we analyze whether the four major HDD manufacturers represented in the dataset -- HGST, Seagate, Toshiba, Western Digital (WD) -- show differences in short- to medium-term HDD failure…

Task-oriented semantic communication must allocate scarce radio resources to semantic features under fast fading wireless conditions and strict end-to-end latency budgets. Existing solutions are either optimization-heavy, leading to…

Linked lists are one of the most basic data structures in computer science. But when many threads try to use the same linked list at the same time, things get complicated. In this paper, we look at five different ways to make a linked list…

Most work on efficient concurrent ordered indices, such as concurrent binary search trees, B-trees, skip lists, etc., has focused on data structures that provide good \emph{worst-case} guarantees. In real workloads, objects are often…

Metaheuristic-based global optimization with embedded, long-running simulations is a computationally expensive process. To support various stages of development and execution, a seamless transition from personal computers to distributed…

Matrix-orthogonalization-based optimizers, exemplified by Muon, have demonstrated strong convergence behavior across a wide range of modern deep learning workloads. The matrix-aware updates offer a compelling alternative to conventional…

Large language model (LLM) post-training for reasoning increasingly relies on reinforcement learning with verifiable rewards (RLVR), where models learn from ground-truth feedback on mathematical, logical, and scientific tasks. To enable…

State-of-the-art AI training simulators assume homogeneous compute and network infrastructure. However, real-world training infrastructure is becoming increasingly heterogeneous since: (a) Model architectures such as multimodal and MoE…

Mixture-of-Experts (MoE) architectures scale large language models (LLMs) to hundreds of billions of parameters. Serving a single MoE model requires multiple GPUs operating in parallel, typically through tensor parallelism (TP) or expert…

Serverless Functions-as-a-Service providers have grown in their offering since inception a decade ago, with a myriad of new functionalities offered to end-users. These new features have also brought new, varied and at times complex pricing…

This paper presents a Qiskit-compatible distributed quantum approximate optimization algorithm (DQAOA) simulator for quadratic unconstrained binary optimization (QUBO) problems arising in engineering design and decision applications. The…

DAG based Byzantine Fault Tolerant protocols provide high throughput consensus under partial synchrony but existing DAG protocols still require at least three message delays to commit decisions. In contrast fast path Byzantine Fault…