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Alexander Böttcher avatar

Seoul VMM and the new VM interface

In January I presented the ongoing work at the FOSDEM 2019 about the generalization of the virtualization interface on Genode for x86. Now the first bunch of commits entered Genode master for the Seoul VMM.

Now I got asked repeatedly, how is the performance on Genode@NOVA compared to the former kernel-specific Seoul VMM version. How do the other supported platforms, e.g. Genode@seL4 and Genode@Fiasco.OC, perform compared to Genode@NOVA, how stable is the kernel-agnostic Seoul VMM on the various platforms, how these and that ...

In the post I try to answer some of the questions. Please keep in mind, that the current state is not heavily optimized for each platform (nor do I attempt or plan to do so), and this post is more or less just an attempt to archive/document the current state. The following measurements may become handy in the future whenever we make adjustments to the VM session, or the Seoul VMM and we afterwards want to know whether things now became better or worse.

The overall subjective perception of the interactive performance seems to have not changed for Genode@NOVA. Using the test browser VM ( seems to behave as before (using Genode 19.02). With the next Sculpt release we actually will see, how well this behaves in a day-to-day setup.

However, the human perception is hard to quantify. To have a more quantifiable load, regarding performance and stability, I removed the dust of the script. The scenario boots up a 32bit Linux VM which compiles a Linux kernel and measures the time it took. Having this running on Genode@NOVA, Genode@Fiasco.OC and Genode@seL4 and across several machines should give us some better comparable values. The branch I used is current master + 3 additional commits which will enter the next Genode master branch eventually.

The changes to a generalized VM session interface took, if at all, some minor performance degradations, as you may see from the tables. The row with the name "nova (19.02)" denotes the numbers as measured on Genode 19.02, so the Seoul VMM version not using the common Genode VM interface. In the 19.02 Seoul version the VMM and the vCPU were placed ever on logical disjunct CPUs. The columns with 1(d) and 2(d) denotes this fact of having 1 vCPU or 2 vCPUs and the VMM on another disjunct logical CPU.

In contrast - the 1(s) and 2(s) columns for the new Seoul VMM version denotes the fact, that the first vCPU and Seoul VMM share the same logical CPU, and any subsequent (2.) vCPU is running on another disjunct logical CPU.

Why do I make this disjunction ? The workload is heavily CPU bound, compiling and storing everything in RAM and no I/O to real physical storage. That means by moving a vCPU exclusively to a physical CPU, you should see some performance improvements. All the additional overhead by the VMM, like processing Genode related events are done on another physical CPU. If you look at the numbers for 1(s) vs 1(d) you will see this at least on weaker CPUs when using few vCPUs.

The good news in general is, that the Seoul VMM is now running on seL4, Fiasco.OC and NOVA. The Linux VM kernel compile succeeds on NOVA and Fiasco.OC using the Seoul VMM adjusted to the kernel agnostic VM interface of Genode. And - Intel and AMD are working for both kernels.

The bad news is that on seL4 the Linux kernel compile does not succeed, because of several reasons. On the one hand our support for seL4 is still experimental (limited allocators in core, only 4k memory mappings), which leads to decreased performance. Additionally, the Linux compile aborts on seL4 with some segmentation faults in the guest after a while. Using multiple vCPUs crash the seL4 kernel reliable. So, obviously something is still wrong and needs more investigations on various levels.

Ok, lets start with what is working. I did some measurements on 3 Intel based machines in the office and 1 AMD machine I privately own. Genode/Seoul/kernel are running 64bit and the VM is a 32 bit guest, as mentioned earlier. For each run I saved the log and added it as reference in the tables. For the percentage numbers I used as base/best number the 1(d) measurement. A positive percentage means that something run longer and therefore it was slower. A negative percentage means that something run shorter and therefore it was faster.

Lenovo X201 - i5 CPU M 520 @ 2.40GHz

For the measurements I disabled the hyperthreads, that means we have 2 physical CPUs. As you see from the numbers, the 19.02 version and the current version of the Seoul VMM for NOVA don't vary a lot. As mentioned, the 1(s) vs 1(d) numbers, show that the co-location of vCPU and VMM on the same physical CPU costs you some performance.

The Fiasco.OC numbers are somewhat slower. Interestingly, the actual expected performance increase of using 1(d) is not observable for Fiasco.OC. The reasons are not clear currently.

kernel/vcpu 1 (s) 1 (d) 2 (s) 2 (d)
nova (19.02) - 717s (+0%) - 396s (-45%)
nova 754s (+5%) 719s (+0%) 399s (-44%) -
foc 869s (+21%) 964s (+34%) 547s (-24%) -
sel4 - - - -

Table 1: Lenovo X201: Linux kernel compile in a 32bit Linux VM

Lenovo T420 - i5-2430M CPU @ 2.40GHz

For the measurements I disabled the hyperthreads, that means we have 2 physical CPUs. The numbers are actually very similar to the X201. One notable difference is, that the measurements for Fiasco.OC on this machine vary a lot between runs. So, they were not as stable as for the NOVA runs.

kernel/vcpu 1 (s) 1 (d) 2 (s) 2 (d)
nova (19.02) - 719s (+0%) - 393s (-45%)
nova 757s (+5%) 722s (+0%) 394s (-45%) -
foc 874s (+21%) 1180s (+64%) 482s (-32%) -
sel4 - - - -

Table 2: Lenovo T420: Linux kernel compile in a 32bit Linux VM

Nightly test machine - i7-4770 CPU @ 3.40GHz

This machine we use nightly to test/boot automated all our x86 based autopilot run scripts on the current staging Genode branch. When it was unused by our automated test infrastructure, I did the measurements with the mentioned branch above.

The machine has 4 physical CPUs, hyperthreading was enabled - so we have up to 8 logical CPUs in the logs. Nevertheless, I used only up to 4 logical CPUs in the runs.

As you see nicely, as more vCPUs you add as quicker your compile finishes. This is observable on both kernels, however some anomaly you can see for Fiasco.OC. The reasons are not clear currently and were not investigated. Again, the numbers between runs are not very stable across runs for Fiasco.OC.

kernel/vcpu 1 (s) 1 (d) 2 (s) 2 (d) 3 (s) 3 (d) 4 (s) 4 (d)
nova (19.02) - 402s (+0%) - 212s (-47%) - 149s (-63%) - 116s (-71%)
nova 416s (+3%) 403s (+0%) 218s (-46%) 214s (-47%) 151s (-62%) 149s (-63%) 119s (-70%)
foc 448s (+11%) 447s (+11%) 261s (-35%) 235s (-42%) 292s (-27%) 167s (-34%) 209s (-48%)
sel4 - - - - - - - -

Table 3: Genode's nighly test machine: Linux kernel compile in a 32bit Linux VM

AMD Phenom II X4 965

I have an oldish AMD machine still around, which I use from time to time to test Genode on privately. The actual good news is, that Seoul runs in principle on AMD actually, even for both kernels.

For Fiasco.OC the numbers are not very stable currently. As you may see from the logs, the time past in the VM is somehow off compared to the time the remote host reported of the run, e.g. 2(s). Because of that I marked the numbers with a star. Obviously, the time virtualization is still wrong, but currently it is not clear on which level (kernel vs VM interface vs Seoul VMM).

kernel/vcpu 1 (s) 1 (d) 2 (s) 2 (d)
nova (19.02) - - - -
nova 614s (+7%) 572s (+0%) 311s (-45%) -
foc 672s (+17%) 981s* (+71%) 480s* (-16%) -
sel4 - - - -

Table 4: AMD Phenom II X4: Linux kernel compile in a 32bit Linux VM


As you can see, things are working quite well. Still, several anomalies exist which require deeper investigations. I'm not going to do that in the close future, because of other more pressing work. If anyone else has time, motivation and technical knowledge I can lend a helping hand.