“Volunteer computing” is the use of consumer digital devices for high-throughput scientific computing. It can provide large computing capacity at low cost, but presents challenges due to device heterogeneity, unreliability, and churn. BOINC, a widely-used open-source middleware system for volunteer computing, addresses these challenges. We describe BOINC’s features, architecture, implementation, and algorithms.

As a major approach to looking for life beyond the Earth, the search for extraterrestrial intelligence (SETI) is committed to searching for technosignatures such as engineered radio signals that are indicative of technologically capable life. In this paper, we report a targeted SETI campaign employing an observation strategy named multibeam coincidence matching at the Five-hundred-meter Aperture Spherical radio Telescope toward 33 known exoplanet systems, searching for ETI narrowband drifting signals across 1.05–1.45 GHz in two orthogonal linear polarization directions separately. A signal at 1140.604 MHz detected from the observation toward Kepler-438 originally piqued our interest because its features are roughly consistent with assumed ETI technosignatures. However, evidences such as its polarization characteristics are able to eliminate the possibility of an extraterrestrial origin. Our observations achieve an unprecedented sensitivity because the minimum equivalent isotropic radiated power we are able to detect reaches 1.48 × 10⁹ W.

SETI@home is a radio Search for Extraterrestrial Intelligence (SETI) project that looks for technosignatures in data recorded at the Arecibo Observatory. The data were collected over a period of 14 yr and cover almost the entire sky visible to the telescope. The first stage of data analysis found billions of detections: brief excesses of continuous or pulsed narrowband power. The second stage removed detections that were likely radio frequency interference (RFI), then identified and ranked signal candidates: groups of detections, possibly spread over the 14 yr, that plausibly originate from a single cosmic source. We manually examined the top-ranking signal candidates and selected a few hundred. In the third and final stage, we are reobserving the corresponding sky locations and frequency ranges using the Five-hundred-meter Aperture Spherical Telescope radio telescope. This paper covers SETI@home’s second stage of data analysis. We describe the algorithms used to remove RFI and to identify and rank signal candidates. To guide the development of these algorithms, we used artificial candidate birdies that model persistent ET signals with a range of power, bandwidth, and planetary motion parameters. This approach also allowed us to estimate the sensitivity of our detection system to these signals.

SETI@home is a radio Search for Extraterrestrial Intelligence (SETI) project, looking for technosignatures in data recorded at multiple observatories from 1998–2020. Most radio SETI projects analyze data using dedicated processing hardware. SETI@home uses a different approach: time-domain data is distributed over the internet to >10⁵ volunteered home computers, which analyze it. The large amount of computing power this affords (∼10¹⁵ floating-point operations per second (FPOP s^–1)) allows us to increase the sensitivity and generality of our search in three ways. We use coherent integration, a technique in which data is transformed so that the power of drifting signals is confined to a single discrete Fourier transform (DFT) bin. We perform this coherent search over 123,000 Doppler drift rates in the range (±100 Hz s⁻¹). Second, we search for a variety of signal types, such as pulsed signals and arbitrary repeated waveforms. The analysis uses a range of DFT sizes, with frequency resolutions ranging from 0.075–1221 Hz. The front end of SETI@home produces a set of detections that exceed thresholds in power and goodness of fit. We accumulated ∼1.2 × 10¹⁰ such detections. The back end of SETI@home takes these detections, identifies and removes radio frequency interference, and looks for groups of detections that are consistent with extraterrestrial origin and that persist over long timescales. This paper describes the front end of SETI@home and provides parameters for the primary data source, the Arecibo Observatory; the back end and its results are described in a companion paper.

Volunteer computing uses millions of consumer computing devices (desktop and laptop computers, tablets, phones, appliances, and cars) to do high-throughput scientific computing. It can provide Exa-scale capacity, and it is a scalable and sustainable alternative to data-center computing. Currently, about 30 science projects use volunteer computing in areas ranging from biomedicine to cosmology. Each project has application programs with particular hardware and software requirements (memory, GPUs, VM support, and so on). Each volunteered device has specific hardware and software capabilities, and each device owner has preferences for which science areas they want to support. This leads to a scheduling problem: how to dynamically assign devices to projects in a way that satisfies various constraints and that balances various goals. We describe the scheduling policy used in Science United, a global manager for volunteer computing.