Name
Abstract | ||
|---|---|---|
The current design trend in large scale machine learning is to use distributed clusters of CPUs and GPUs with MapReduce-style programming. Some have been led to believe that this type of horizontal scaling can reduce or even eliminate the need for traditional algorithm development, careful parallelization, and performance engineering. This paper is a case study showing the contrary: that the benefits of algorithms, parallelization, and performance engineering, can sometimes be so vast that it is possible to solve \"cluster-scale\" problems on a single commodity multicore machine. Connectomics is an emerging area of neurobiology that uses cutting edge machine learning and image processing to extract brain connectivity graphs from electron microscopy images. It has long been assumed that the processing of connectomics data will require mass storage, farms of CPU/GPUs, and will take months (if not years) of processing time. We present a high-throughput connectomics-on-demand system that runs on a multicore machine with less than 100 cores and extracts connectomes at the terabyte per hour pace of modern electron microscopes. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3018743.3018766 | PPOPP |
Keywords | Field | DocType |
Multicore Programming,Machine Learning,Big-Data,Connectomics | Pace,Connectomics,Performance engineering,On demand,Computer science,Terabyte,Parallel computing,Image processing,Theoretical computer science,Multi-core processor,Mass storage | Conference |
Volume | Issue | ISSN |
52 | 8 | 0362-1340 |
Citations | PageRank | References |
2 | 0.45 | 33 |
Authors | ||
9 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Matveev | 1 | 110 | 7.75 |
| Yaron Meirovitch | 2 | 25 | 3.33 |
| Hayk Saribekyan | 3 | 2 | 0.78 |
| Wiktor Jakubiuk | 4 | 2 | 0.45 |
| Tim Kaler | 5 | 37 | 4.74 |
| Gergely Ódor | 6 | 11 | 2.14 |
| David Budden | 7 | 2 | 0.45 |
| aleksandar zlateski | 8 | 39 | 5.65 |
| Nir Shavit | 9 | 3780 | 244.84 |