AI Summary

• The text appears to be a solution to a machine learning problem involving kernel ridge regression and random Fourier features. [3]
• This may involve simplifying complex equations, identifying patterns or relationships between variables, and applying mathematical techniques such as integration and differentiation. [3]
• The problem statement is not provided, so I'll provide a general outline of how to approach it. [2]
• To solve this problem, we need to follow the steps outlined in the provided text. [1]

Abstract
This work develops a unified {learning- and information-theoretic} framework for distributed computation and inference across multiple users and servers. The proposed \emph{General Multi-User Distributed Computing (GMUDC)} model characterizes how computation, communication, and accuracy can be jointly optimized when users demand heterogeneous target functions that are arbitrary transformations of shared real-valued subfunctions. Without any separability assumption, and requiring only that each target function lies in a reproducing-kernel Hilbert space associated with a shift-invariant kernel, the framework remains valid for arbitrary connectivity and task-assignment topologies. A dual analysis is introduced: the \emph{quenched design} considers fixed assignments of subfunctions and network topology, while the \emph{annealed design} captures the averaged performance when assignments and links are drawn uniformly at random from a given ensemble. These formulations reveal the fundamental limits governing the trade-offs among computing load, communication load, and reconstruction distortion under computational and communication budgets~$Γ$ and~$Δ$. The analysis establishes a spectral-coverage duality linking generalization capability with network topology and resource allocation, leading to provably efficient and topology-aware distributed designs. The resulting principles provide an \emph{information-energy foundation} for scalable and resource-optimal distributed and federated learning systems, with direct applications to aeronautical, satellite, and edge-intelligent networks where energy and data efficiency are critical.

Why we think this paper is great for you:
This paper develops a unified framework for distributed computation across multiple users and servers, which is a core area of distributed systems research. It characterizes how computation, communication, and accuracy can be jointly optimized.

AI Summary

• It accurately captures latency distributions under various system conditions, including contention between multiple users. [3]
• UL latency: uplink latency, which is the time it takes for data to travel from the user equipment (UE) to the base station (gNB). [3]
• The model's ability to capture complex interdependencies between system parameters and latency distributions makes it a valuable tool for network designers and engineers. [3]
• The model abstracts away inter-user contention to keep the analysis tractable and focuses on modeling hardware processing time, transmission times, and other non-deterministic components. [2]
• LatencyScope is an analytical model for estimating latency in 5G networks. [1]

Abstract
This paper presents LatencyScope, a mathematical framework for accurately computing one-way latency (for uplink and downlink) in the 5G RAN across diverse system configurations. LatencyScope models latency sources at every layer of the Radio Access Network (RAN), pinpointing system-level bottlenecks--such as radio interfaces, scheduling policies, and hardware/software constraints--while capturing their intricate dependencies and their stochastic nature. LatencyScope also includes a configuration optimizer that uses its mathematical models to search through hundreds of billions of configurations and find settings that meet latency-reliability targets under user constraints. We validate LatencyScope on two open-sourced 5G RAN testbeds (srsRAN and OAI), demonstrating that it can closely match empirical latency distributions and significantly outperform prior analytical models and widely used simulators (MATLAB 5G Toolbox, 5G-LENA). It can also find system configurations that meet Ultra-Reliable Low-Latency Communications (URLLC) targets and enable network operators to efficiently identify the best setup for their systems.

Why we think this paper is great for you:
This paper offers a mathematical framework for accurately computing and identifying latency bottlenecks in 5G RAN, providing deep insights into system-level performance. It is highly relevant for understanding and improving low latency systems.

Abstract
5G Standalone (SA) is the goal of the 5G evolution, which aims to provide higher throughput and lower latency than the existing LTE network. One of the main applications of 5G is the real-time distribution of Ultra High-Definition (UHD) content with a resolution of 4K or 8K. In Q2/2021, Advanced Info Service (AIS), the biggest operator in Thailand, launched 5G SA, providing both 5G SA/NSA service nationwide in addition to the existing LTE network. While many parts of the world are still in process of rolling out the first phase of 5G in Non-Standalone (NSA) mode, 5G SA in Thailand already covers more than 76% of the population. In this paper, UHD video will be a real-time live streaming via MPEG-DASH over different mobile network technologies with minimal buffer size to provide the lowest latency. Then, performance such as the number of dropped segments, MAC throughput, and latency are evaluated in various situations such as stationary, moving in the urban area, moving at high speed, and also an ideal condition with maximum SINR. It has been found that 5G SA can deliver more than 95% of the UHD video segment successfully within the required time window in all situations, while 5G NSA produced mixed results depending on the condition of the LTE network. The result also reveals that the LTE network failed to deliver more than 20% of the video segment within the deadline, which shows that 5G SA is absolutely necessary for low-latency UHD video streaming and 5G NSA may not be good enough for such task as it relies on the legacy control signal.

Why we think this paper is great for you:
This paper evaluates low-latency live streaming and higher throughput in 5G networks, providing practical performance insights into real-time content distribution. It is highly relevant for applications requiring both low latency and high throughput.

Abstract
We study an extension of the standard two-party communication model in which Alice and Bob hold probability distributions $p$ and $q$ over domains $X$ and $Y$, respectively. Their goal is to estimate \[ \mathbb{E}_{x \sim p,\, y \sim q}[f(x, y)] \] to within additive error $\varepsilon$ for a bounded function $f$, known to both parties. We refer to this as the distributed estimation problem. Special cases of this problem arise in a variety of areas including sketching, databases and learning. Our goal is to understand how the required communication scales with the communication complexity of $f$ and the error parameter $\varepsilon$. The random sampling approach -- estimating the mean by averaging $f$ over $O(1/\varepsilon^2)$ random samples -- requires $O(R(f)/\varepsilon^2)$ total communication, where $R(f)$ is the randomized communication complexity of $f$. We design a new debiasing protocol which improves the dependence on $1/\varepsilon$ to be linear instead of quadratic. Additionally we show better upper bounds for several special classes of functions, including the Equality and Greater-than functions. We introduce lower bound techniques based on spectral methods and discrepancy, and show the optimality of many of our protocols: the debiasing protocol is tight for general functions, and that our protocols for the equality and greater-than functions are also optimal. Furthermore, we show that among full-rank Boolean functions, Equality is essentially the easiest.

Why we think this paper is great for you:
This paper explores the communication complexity within a distributed estimation model, which is fundamental to understanding efficiency in distributed systems. It offers insights into the theoretical underpinnings of distributed computation.

AI Summary

• The Pickle Prefetcher can effectively leverage the LLC (Last-Level Cache) to improve performance, capturing 62.91% and 58.37% of the potential performance gain for the berkstan and roadNetCA graphs, respectively. [3]
• The results highlight that a timely prefetch ratio above 40% can lift all slowdowns to speedups. [3]
• The Pickle Prefetcher achieves the goal of consistently lowering the memory access latency across all graphs, confirming that the workload is indeed memory-latency bounded. [3]
• It is typically the largest and slowest cache level. [3]
• Prefetcher: A hardware component or software mechanism that predicts which data will be needed by the processor in the near future and brings it into the cache before it is actually requested. [3]
• A high timely prefetch percentage is necessary but not sufficient for higher performance. [2]

Abstract
Modern high-performance architectures employ large last-level caches (LLCs). While large LLCs can reduce average memory access latency for workloads with a high degree of locality, they can also increase latency for workloads with irregular memory access patterns. Prefetchers are widely used to reduce memory latency by prefetching data into the cache hierarchy before it is accessed by the core. However, existing prediction-based prefetchers often struggle with irregular memory access patterns, which are especially prevalent in modern applications. This paper introduces the Pickle Prefetcher, a programmable and scalable LLC prefetcher designed to handle independent irregular memory access patterns effectively. Instead of relying on static heuristics or complex prediction algorithms, Pickle Prefetcher allows software to define its own prefetching strategies using a simple programming interface without expanding the instruction set architecture (ISA). By trading the logic complexity of hardware prediction for software programmability, Pickle Prefetcher can adapt to a wide range of access patterns without requiring extensive hardware resources for prediction. This allows the prefetcher to dedicate its resources to scheduling and issuing timely prefetch requests. Graph applications are an example where the memory access pattern is irregular but easily predictable by software. Through extensive evaluations of the Pickle Prefetcher on gem5 full-system simulations, we demonstrate tha Pickle Prefetcher significantly outperforms traditional prefetching techniques. Our results show that Pickle Prefetcher achieves speedups of up to 1.74x on the GAPBS breadth-first search (BFS) implementation over a baseline system. When combined with private cache prefetchers, Pickle Prefetcher provides up to a 1.40x speedup over systems using only private cache prefetchers.

Why we think this paper is great for you:
This paper focuses on reducing memory access latency and enhancing performance in high-performance architectures, which is crucial for achieving low latency and high throughput. It presents a scalable prefetcher design to improve system responsiveness.