Tux3 file system

For this, use "verbose" build mode. This is especially true for the development releases, since each new release contains new code which has not been debugged. Make sure you keep a backup of the modules corresponding to that kernel, as well. To use the new kernel, save a copy of the old image and copy the new image over the old one. If you don't, you won't be able to boot the new kernel image.

See the LILO docs for more information. After reinstalling LILO, you should be all set. Shutdown the system, reboot, and enjoy! If you ever need to change the default root device, video mode, ramdisk size, etc. No need to recompile the kernel to change these parameters. If there isn't anyone listed there, then the second best thing is to mail them to me torvalds linux-foundation. If the problem is new, tell me so, and if the problem is old, please try to tell me when you first noticed it.

The dump may look incomprehensible to you, but it does contain information that may help debugging the problem. The text above the dump is also important: it tells something about why the kernel dumped code in the above example, it's due to a bad kernel pointer. Alternatively, you can do the dump lookup by hand: - In debugging dumps like the above, it helps enormously if you can look up what the EIP value means. The hex value as such doesn't help me or anybody else very much: it will depend on your particular kernel setup.

What you should do is take the hex value from the EIP line ignore the "" , and look it up in the kernel namelist to see which kernel function contains the offending address. To find out the kernel function name, you'll need to find the system binary associated with the kernel that exhibited the symptom.

To extract the namelist and match it against the EIP from the kernel crash, do: nm vmlinux sort less This will give you a list of kernel addresses sorted in ascending order, from which it is simple to find the function that contains the offending address. Note that the address given by the kernel debugging messages will not necessarily match exactly with the function addresses in fact, that is very unlikely , so you can't just 'grep' the list: the list will, however, give you the starting point of each kernel function, so by looking for the function that has a starting address lower than the one you are searching for but is followed by a function with a higher address you will find the one you want.

In fact, it may be a good idea to include a bit of "context" in your problem report, giving a few lines around the interesting one. If you for some reason cannot do the above you have a pre-compiled kernel image or similar , telling me as much about your setup as possible will help. You can now use all the usual gdb commands. About tux3 file system Resources Readme.

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We urge you not to use the CDDL for this reason. This is why f. Are you serious? Heatmap of the Hamming distances. If we think of each file systems as a string of bits with one indicating the presence of a certain external symbol and zero as the absence of it then the Hamming distance is the minimum number of substitutions necessary to change one string into another. The heatmap is symmetric. The red indicates a Hamming distance of zero, which corresponds to maximum similarity, and yellow indicates very little similarity.

Everyone is similar with itself so the diagonal is red. In this plot the file systems are sorted in ascending order of their number of external symbols. What we are going to do next is to reorder the rows and columns in a way that brings closer the file systems that are similar.

Hierarchical clustering. As the name implies, hierarchical clustering builds a hierarchy of clusters. There are two ways to do this: one is to start from bottom, with all the data points as clusters and then, at each step, merge two of them.

This is also know as agglomerative nesting. The other one is called divisive and starts from top, with everything in a big cluster, and at each steps performs a split. Below is an example of clustering 11 points situated in a 2D plane. Left is a dendrogram , a tree diagram usually used to represent the result of a hierarchical clustering.

Right is a representation using nested clusters. Single linkage. When all the clusters only contains one point we can easily define the distance between them as the distance d between the respective points. After each merge operation we need a way to define the distance D between this new cluster and all the old ones. One way to do this is to consider the distance between two clusters as the minimum distance between any pair of nodes with one node in a cluster and another one in the other.

This method is best suited for constructing elongated clusters like the ones below. Complete linkage. In this case the distance between clusters is defined as the maximum distance between the pairs of nodes which contain one node from one cluster and one from the other.

This method is capable of creating small and compact clusters. Group average. We can also define the distance between two clusters in such a way that all the pairwise distances contribute to the result. If we take the average of all the pairs then the method is called group average. Ward's method. This method works like this: for each cluster we compute the sum of squared deviations from the cluster's centroid. Then we sum up all these sums and get a total error sum.

At each step we merge the two clusters which minimize the increase of total error sum. McQuitty's method. In this method, after each merge, the distance between the new cluster and the old ones are computed based on the distances of the two clusters that were merged.

In the example below, two clusters, A and B, were merge and formed a new cluster E. Clustering using the Hamming distance and Ward's method.

Let's now take a look at how the heatmap of the Hamming distances we introduced earlier looks like when we reorder it using Ward's algorithm. Dendrogram of the clustering using Hamming distance and Ward's method. If we look only at the dendrogram we can notice that this method divided the file systems in two big parts: complex disk-based systems including cluster file systems which contain a disk-based part inside them , and everything else.

We can also observe that most of the ancient file system category is contained almost completely in one big branch fat — minix. Some unusual matches: 9p is situated quite far from the rest of the network file systems and tux3 ends up keeping company to the group of ancient file systems. In contrast, btrfs enjoys the neighborhood of xfs and gfs2 in the upper class of complex file systems. Clustering using the Hamming distance and complete linkage. Unlike the previous method, the rearrangement using the clustering based on furthest neighbor strategy also known as complete linkage creates a heatmap with a nicely defined center.

The kernel is formed by a group of ancient file system and a few memory-based file systems. Dendrogram of the clustering using the Hamming distance and complete linkage. Here we can see that the big nice split from Ward's method is replaced by a more scatter division. The complex disk-based file systems are now split in two parts, ocfs2 — ext3 and xfs — ubifs , the second of them being muddle by two flash-based file systems jffs2 and ubifs.

Like before, tux3 is close to a bunch of ancient file systems.