Search arXivSearch

arXiv · 1501.07079

The Affinity Effects of Parallelized Libraries in Concurrent Environments

Abstract

The use of cloud computing grows as it appears to be an additional resource for High-Performance Parallel and Distributed Computing (HPDC), especially with respect to its use in support of scientific applications. Many studies have been devoted to determining the effect of the virtualization layer on the performance, but most of the studies conducted so far lack insight into the joint effects between application type, virtualization layer and parallelized libraries in applications. This work introduces the concept of affinity with regard to the combined effects of the virtualization layer, class of application and parallelized libraries used in these applications. Affinity is here defined as the degree of influence that one application has on other applications when running concurrently in virtual environments hosted on the same real server. The results presented here show how parallel libraries used in application implementation have a significant influence and how the combinations between these types of libraries and classes of applications could significantly influence the performance of the environment. In this context, the concept of affinity is then used to evaluate these impacts to contribute to better stability and performance in the computational environment.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fabio Licht, Bruno Schulze, Luis E. Bona, Antonio R. Mury. 2015-01-28. The Affinity Effects of Parallelized Libraries in Concurrent Environments. https://arxiv.org/abs/1501.07079

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Fast and Robust Information Spreading in the Noisy PULL Model

Efficient information spreading in stochastic multi agent systems is a core challenge when communication is noisy, bandwidth limited, and agents lack global coordination. Yet biological systems, including ant colonies and fish schools, routinely overcome these constraints. A small number of informed individuals can reliably guide large, uncoordinated populations using minimal, noisy signals. Motivated by these observations, we study how reliable information dissemination can be achieved in such bio inspired settings. A population of $n$ agents, each with a binary preference, includes a designated subset of source agents, and the goal is to converge to the majority preference among the sources. In the noisy $\mathcal{PULL}(h)$ model, each agent observes noisy messages from $h$ randomly sampled peers in every round. Prior work shows that convergence requires $Ω(n/h)$ rounds even under favorable conditions. We ask how far we can push simplicity, with no synchronization at the start time and minimal message size, without compromising convergence speed. We present a quasi self-stabilizing protocol using only 2-bit messages that converges from arbitrary initial states despite severe noise and initial asynchrony. It achieves optimal convergence time $O((n/h)\log n)$ with high probability, and in particular $O(\log n)$ time in the snapshot regime $h=Θ(n)$. A key subroutine is an even simpler 1-bit protocol assuming simultaneous start, based on a natural two phase listen then amplify mechanism. Together, our results show that simple, biologically inspired protocols can achieve optimal and robust information dissemination even in highly unreliable and uncoordinated systems.

cs.DC

LOIP:Collaborative Lossless LLM Inference Serving with Offloading-based Pipeline Parallelism on Edge Devices

Providing lossless inference services of LLMs on edge devices remains challenging, especially given the extremely tight memory budgets. The existing offloading techniques inevitably introduce numerous loading bubbles, which further inflate the end-to-end latency of the entire inference pipeline. Meanwhile, dynamically fluctuating network bandwidth and diverse user request patterns pose additional obstacles to efficient lossless inference on edge devices. To address this, we propose LOIP, a collaborative lossless LLM inference system that employs an offloading-based interleaved pipeline parallelism to better overlap model offloading with computing and communicating. Specifically, LOIP first constructs an offloading-aware cost model to characterize inference latency and memory overhead under heterogeneous device capabilities and limited bandwidth. Based on this cost model, LOIP develops a fine-grained allocation scheduler that determines latency-efficient layer partitions across devices while explicitly accounting for offloading overhead, along with a unified memory architecture (UMA)-aware loading optimization using customized CUDA operators to reduce runtime loading overhead. LOIP further designs an online memory adaptation strategy to handle the increasing KV cache pressure and dynamic bandwidth fluctuations during inference. We implement LOIP with 2500+ lines of Python and 500+ lines of C++/CUDA code, and deploy it on five heterogeneous NVIDIA Jetson edge devices for lossless collaborative inference of LLaMA3.3-70B-Instruct. Extensive experiments demonstrate that LOIP achieves 8.8$\times$$\sim$20.3$\times$ speedups over the SOTA baselines under different bandwidth conditions and request patterns without compromising model accuracy.

cs.DC

Fluid Notarization: Verifiable Evolution of Concurrently Edited Structured Documents

Traditional blockchain-based document notarization follows a snapshot-oriented model in which each document revision is represented as an independent state anchored on-chain through a cryptographic reference. While effective for immutable artifacts, this approach becomes inadequate when documents evolve through collaborative editing. Concurrent modifications create divergent document versions that must be reconciled outside the notarization layer, while even minor changes require generating and distributing new document snapshots. Conversely, collaborative replication frameworks such as CRDTs provide deterministic reconciliation of concurrent updates, but do not inherently provide independently verifiable evidence of when contributions were published. This paper introduces Fluid Notarization, a notarization paradigm in which document evolution itself becomes the object of notarization. Rather than certifying isolated states, Fluid Notarization certifies a graph of causally related evolution artifacts generated by a JSON-native delta-CRDT. The proposed model builds upon Melda, which represents document changes as compact, content-addressed deltas linked through causal dependencies. Blockchain notarization is reduced to recording identifiers of these evolution artifacts, while synchronization, reconstruction, and conflict resolution remain entirely off-chain. The resulting architecture combines two complementary guarantees: deterministic convergence provided by the CRDT and independently auditable proof-of-existence, provenance, and publication evidence provided by the blockchain. A prototype implementation and validation scenario based on collaboratively edited electronic health records demonstrate the feasibility of the approach and highlight the advantages of notarizing document evolution rather than successive document snapshots.

cs.DC