A Multicore Path to Connectomics-on-Demand

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 mi-croscopy 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.

Toward A Reproducible, Scalable Framework for Processing Large Neuroimaging Datasets

“…The SegEM framework [9] offers extensive features for optimizing and deploying EM pipelines, but is specifically focused on neuron segmentation from EM data and is tied to a MATLAB cluster implementation. Highly optimized pipelines can be deployed on a single workstation [10], which is ideal for proven pipelines as part of ongoing data collection, but is limited in developing and benchmarking new pipelines.…”

“…Several workflows exist to produce graphs of brain connectivity from EM data [6,10,7], including an approach that optimizes each stage in the processing pipeline based on end-to-end performance [8]. However, these tools were not standardized into a reproducible processing environment, making reproduction of results and comparison of new algorithms challenging.…”

“…Traditional techniques and pipelines developed and validated on smaller datasets may not easily transfer to datasets that are acquired by a different laboratory or that are too large to analyze on a single computer or with a single script. Prior machine vision pipelines for EM processing, for instance, have had considerable success [6,7,8,9,10]. However, these pipelines may require extensive configuration and are not scalable [8], may require proprietary software and have unknown hyperparameters [9], or are highly optimized for a single hardware platform [10].…”

Cilkmem: Algorithms for Analyzing the Memory High-Water Mark of Fork-Join Parallel Programs

“…In addition to measuring Cilkmem's performance overhead, we use Cilkmem to analyze a big-data application, specifically, an image-alignment program [20] used for brain connectomics [25]. Section 6 describes how, for this application, Cilkmem reveals a previously unknown issue contributing to unexpectedly high memory usage under parallel executions.…”

Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics

“…Many of these techniques use the hierarchical approach of Andres et al [2] that employs the well-known hierarchical image segmentation framework [3,15,39,47]. This is still the most common approach in connectomics segmentation pipelines: first detecting object borders in 2-D/3-D and then gradually agglomerating information to form the final objects [6,9,20,27,31,34,35,52]. The elevation maps obtained from the border detectors are treated as estimators of the true border probabilities [9], which are used to define an over-segmentation of the image, foreground connected components on top of a background canvas.…”