Superrecursive Features of Interactive Computation

Mark Burgin

Home/Archive/arXiv

arXiv · 0710.1455

Superrecursive Features of Interactive Computation

Mark Burgin

View original source ↗Read source document ↗Export metadata ↓

Abstract

Functioning and interaction of distributed devices and concurrent algorithms are analyzed in the context of the theory of algorithms. Our main concern here is how and under what conditions algorithmic interactive devices can be more powerful than the recursive models of computation, such as Turing machines. Realization of such a higher computing power makes these systems superrecursive. We find here five sources for superrecursiveness in interaction. In addition, we prove that when all of these sources are excluded, the algorithmic interactive system in question is able to perform only recursive computations. These results provide computer scientists with necessary and sufficient conditions for achieving superrecursiveness by algorithmic interactive devices.

Explore related subjects

cs.DC cs.PF

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Mark Burgin. 2007-10-08. Superrecursive Features of Interactive Computation. https://arxiv.org/abs/0710.1455

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

KEEP EXPLORING

Related papers

arXiv2026

VeriNC: Finding Design Risks of In-Network Computing Systems

The emergence of programmable switches has brought in-network computing (INC) into the spotlight in recent years. By offloading computation directly onto the data transmission process, INC improves network utilization, reduces latency to sub-RTT levels, saves link bandwidth, and maintains throughput. However, INC disrupts the transparency of traditional networks, forcing developers to consider network exceptions like packet loss and out-of-order. If not properly handled, these exceptions can lead to violations of application properties, such as cache consistency and lock exclusion. Usual testing cannot exhaustively cover these exceptions, raising doubts about the correctness of INC systems and hindering their deployment in the industry. This paper presents VeriNC, the first general-purpose tool for verifying INC systems. VeriNC provides a high-level specification language and saves developers 67.2% lines of code on average. To help better understand the behavior of the system, VeriNC offers configurable network environments. VeriNC enables developers to express INC-specific correctness properties. VeriNC translates developer-specified systems into state transition representations, performs model checking to detect potential design risks, and reports violation traces to developers. We propose optimizations for INC-specific scenarios to address the challenge of state space explosion. We modeled INC systems across four application domains and identified design risks with VeriNC in seconds. VeriNC has also been adopted to guide the design of a new INC protocol. Based on our verification experience, we summarize lessons that help develop a correct INC protocol. We further reproduce them in real systems to confirm the validity of our verification result.

cs.DC↗

arXiv2026

TorchGWAS 1.0: GPU-accelerated GWAS at scale

Imaging, molecular, and machine-learning workflows can generate thousands of quantitative phenotypes in a single cohort, creating substantial computational and output bottlenecks when testing traits individually. TorchGWAS is a GPU-accelerated framework that uses batched operations for high-throughput, covariate-adjusted linear association testing across large panels of quantitative phenotypes. Across 500,036 allele-harmonized tests, TorchGWAS t statistics agreed with PLINK 2.0. On an NVIDIA H100 80-GB GPU with a 48-core Intel Xeon Gold 6442Y host and measured disk read and write rates of 5.98 and 1.49 GB/s, respectively, median end-to-end times for 4.57 billion associations (8,931,083 variants by 512 phenotypes in 35,365 samples) were 28.46 s for BED, 29.13 s for hard-call PGEN, 51.48 s for BGEN, and 58.95 s for dosage PGEN, including writing 36.7 GB of binary summary statistics. TorchGWAS provides an efficient Python-based framework for parallel fixed-effect association screening at biobank scale.TorchGWAS is implemented in Python and distributed as a documented source repository at https://github.com/ZhiGroup/TorchGWAS.

cs.DC↗

arXiv2026

AI-Assisted Computational Reproducibility on the FABRIC Testbed

Computational reproducibility remains difficult despite being central to scientific research. In this paper, we show how the international FABRIC testbed, combined with a large language model (LLM) coding agent through LoomAI, can simplify reproducing published experiments across multiple domains. We reproduced three case studies on FABRIC, covering BBR-family congestion-control evaluations, LAMMPS molecular dynamics scaling benchmarks on a CPU-only MPI cluster, and stress protein homeostasis genomics pipelines. Rather than focusing only on matching numerical outputs, we evaluate whether the reproduced experiments support the same scientific conclusions as the original studies. The AI assistant was effective in setting up the environment, adapting code, and debugging, but struggled with the analysis stages that lacked clearly defined workflows, which required human guidance to establish execution order and data dependencies. Across the case studies, the AI-assisted workflow reduced reproduction effort by roughly 4--6 times. We conclude with practical recommendations for improving AI-assisted reproducibility on research testbeds.

cs.DC↗