arXiv subjects

Daniel Barcelona-Pons

Publications and source records attributed to Daniel Barcelona-Pons.

3 recordsLinked to original sources

arXiv2026

LiteRAG: Cost-Efficient Graph-Based Retrieval-Augmented Generation

Graph-based retrieval can improve multi-hop question answering, but existing approaches often incur high query-time costs and produce diffuse, oversized contexts that reduce generation efficiency. We present LiteRAG, a graph-based retrieval method that replaces expensive retrieval-time LLM control with query-conditioned algorithmic exploration and reasoning-chain context construction. On DistComp, a benchmark for multi-hop retrieval over distributed-systems papers, LiteRAG attains the highest overall quality among the evaluated methods (0.798) while reducing per-query latency by over 100$\times$ and cost by over 99% relative to GraphRAG Global and DRIFT. On UltraDomain, it matches LinearRAG on overall quality while using about 14$\times$ fewer tokens. An ablation study indicates that LiteRAG's query-adaptive thresholding and community-aware hub penalization are the main drivers of its token-efficiency gains.

cs.IR↗

arXiv2024

FaaS Is Not Enough: Serverless Handling of Burst-Parallel Jobs

Function-as-a-Service (FaaS) struggles with burst-parallel jobs due to needing multiple independent invocations to start a job. The lack of a group invocation primitive complicates application development and overlooks crucial aspects like locality and worker communication. We introduce a new serverless solution designed specifically for burst-parallel jobs. Unlike FaaS, our solution ensures job-level isolation using a group invocation primitive, allowing large groups of workers to be launched simultaneously. This method optimizes resource allocation by consolidating workers into fewer containers, speeding up their initialization and enhancing locality. Enhanced locality drastically reduces remote communication compared to FaaS, and combined with simultaneity, it enables workers to communicate synchronously via message passing and group collectives. This makes applications that are impractical with FaaS feasible. We implemented our solution on OpenWhisk, providing a communication middleware that efficiently uses locality with zero-copy messaging. Evaluations show that it reduces job invocation and communication latency, resulting in a 2$\times$ speed-up for TeraSort and a 98.5% reduction in remote communication for PageRank (13$\times$ speed-up) compared to traditional FaaS.

cs.DC↗

arXiv2021

Benchmarking Parallelism in FaaS Platforms

Serverless computing has seen a myriad of work exploring its potential. Some systems tackle Function-as-a-Service (FaaS) properties on automatic elasticity and scale to run highly-parallel computing jobs. However, they focus on specific platforms and convey that their ideas can be extrapolated to any FaaS runtime. An important question arises: do all FaaS platforms fit parallel computations? In this paper, we argue that not all of them provide the necessary means to host highly-parallel applications. To validate our hypothesis, we create a comparative framework and categorize the architectures of four cloud FaaS offerings, emphasizing parallel performance. We attest and extend this description with an empirical experiment that consists in plotting in deep detail the evolution of a parallel computing job on each service. The analysis of our results evinces that FaaS is not inherently good for parallel computations and architectural differences across platforms are decisive to categorize their performance. A key insight is the importance of virtualization technologies and the scheduling approach of FaaS platforms. Parallelism improves with lighter virtualization and proactive scheduling due to finer resource allocation and faster elasticity. This causes some platforms like AWS and IBM to perform well for highly-parallel computations, while others such as Azure present difficulties to achieve the required parallelism degree. Consequently, the information in this paper becomes of special interest to help users choose the most adequate infrastructure for their parallel applications.

cs.DC↗