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By Mark Russinovich
Andersen shows you how to sink leaf chads in preparation for the AP Biology photosynthesis lab. An empty syringe is used to remove gas from the leaves b. It's a free tool allowing to graphically display the hard disk space usage: it shows all the directories and subdirectories of the hard drive on a single graph, allowing to see the largest directories at a glance.
Published: November 04, 2020
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Introduction
Du (disk usage) reports the disk space usage for the directory youspecify. By default it recurses directories to show the total size of adirectory and its subdirectories.
Using Disk Usage (DU)
Usage: du [-c[t]] [-l <levels> | -n | -v] [-u] [-q]<directory>
| Parameter | Description |
|---|---|
| -c | Print output as CSV. Use -ct for tab delimiting. |
| -l | Specify subdirectory depth of information (default is all levels). |
| -n | Do not recurse. |
| -v | Show size (in KB) of intermediate directories. |
| -u | Count each instance of a hardlinked file. |
| -q | Quiet (no banner). |
Disk Graph For Mac
CSV output is formatted as:
Path, CurrentFileCount, CurrentFileSize, FileCount, DirectoryCount,DirectorySize
Download Du(1.62 MB)
Disk Graph Definition
Out-of-core graph processing systems are well-optimized to maintain sequential locality on disk and minimize the amount of disk I/O per iteration. Even though the sparsity in real-world graphs provides opportunities for out-of-order execution, these systems often process graphs iteration-by-iteration, hence providing Bulk Synchronous Parallel (synchronous for short) mode of processing which is also a preferred choice for easier programmability. Since out-of-core setting limits the view of entire graph and constrains the processing order to maintain disk locality, exploiting out-of-order execution while simultaneously providing synchronous processing guarantees is challenging. In this paper we develop a generic dependency-driven out-of-core graph processing technique, called Lumos, that performs out-of-order execution to proactively propagate values across iterations while simultaneously providing synchronous processing guarantees. Our cross-iteration value propagation technique identifies future dependencies that can be safely satisfied, and actively computes values across those dependencies without sacrificing disk locality. This eliminates the need to load the corresponding portions of graph in future iterations, hence reducing disk I/O and accelerating the overall processing.