This is made possible by reducing the spectral and time resolution by a factor of 16, and using a computer cluster, the Arecibo Signal Processor to search for pulsars in the data.

Re-processing these data with full resolution is, computationally, a very challenging task but is essential for detecting many fast (both young and recycled) pulsars so far hidden by Galactic plasma.

It has been run on all WAPP data archived at the Cornell University Center for Advanced Computing and has provided 2.5 million signal candidates.

The second pipeline is based on PRESTO, a large suite of pulsar search and analysis software developed by Scott Ransom.

The PRESTO pipeline is run on dedicated clusters at several institutions that participate in the ALFA survey, producing over 3 million signal candidates.

Over the past two years, the Guillimin supercomputer, managed by McGill University as part of CLUMEQ, has been processing most of the PALFA data with PRESTO.

The Einstein@Home algorithm is particularly sensitive to radio pulsars in tight binary systems (as short as 11 minutes), with a phase-space coverage that is complementary to that of the PRESTO pipeline.

We are also implementing a variety of heuristics as well as machine learning algorithms for identifying real pulsars among the millions of signal candidates, most of which appear to be due to RFI.

The inevitable growth in the incidence and variety of man-made RFI suggests that this problem will likely be important for all future radio pulsar surveys.

ARCC is an integrated research/education facility that allows students at the high school and undergraduate level to be directly involved with the research at the Arecibo telescope.