Mprovements In Performance For Mac

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  1. Improvements In Performance For Mac
  2. Jobs In Performance Improvement

Mozilla has released Firefox 62 with some minor updates like support for macOS Mojave’s automatic theme switching, a new Firefox Home tab page, support for variable fonts and more. However, for macOS users, the next release, Firefox 63, is the one to look forward to. It is going to include some much needed performance improvements for macOS users, which will improve tab switching and overall graphics usage.

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This should result in a better experience for macOS users who have been complaining about Firefox’s performance issues since a long time, which weren’t fixed even in Firefox Quantum’s initial release. Despite the much hyped Firefox Quantum release, the performance for Mac users was not as good as expected. Windows users were able to enjoy the faster performance, but for Mac users, the browsing experience has been slow. Jittery scrolling, slow tab switching and slideshow like frame-rate when watching videos or using graphically heavy websites were common issues.

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  • Let us know in the comments below if you have any other techniques for improving the performance of your Mac. You can also find some useful Mac apps and utilities on Envato Market. They can help you be more efficient and productive on your Mac, accomplishing everything from compressing images to cleaning up code.

Reddit is filled with complaints about battery drain and over-heating MacBooks when using Firefox Quantum. Some of these issues were attributed to the fact that Firefox did not use CoreAnimation, Mac’s API for talking to the GPU, which made it inefficient. It doesn’t matter whether the Mac had internal or dedicated graphics, the issue could be replicated on both slow and powerful Macs, as long as they used a Retina Display. When visiting a graphically heavy website, watching high quality videos or WebGL based web apps, performance used to take a massive hit. This issue has been fixed and you can read more details. Another issue was the lack of ‘‘ which was released for Windows and Linux with Firefox 61. This feature is what improves tab switching performance.

You can read more about it. If you want to go ahead and test Firefox 63’s performance improvements on your Mac, you can download. Otherwise, you can wait until 23 October, when Mozilla is expected to release version 63 to stable channel i.e. Having used Firefox 63 nightly on my 12″ MacBook, I can confirm that the improvements make a noticeable difference. However, there is still some work to be done before Firefox can feel as fast as Safari or Chrome on Mac. While Firefox is working towards improving things for macOS users, Chrome seems to have reversed course recently.

With the newest release, macOS users have been greeted with a sluggish UI, slow tab switching and overall laggy performance. This is not why users preferred Chrome over other browsers, so hopefully Google will be working on fixing these issues in upcoming updates.

Happy New Year and happy ten year anniversary to this blog, which I totally forgot back in October:-/. Blogging activity here has been so slow that I almost forgot how to work with Hugo. We started 2018 with heavy speculation on critical CPU bugs that were under disclosure embargo.

Luckily for us, Google decided to break the embargo and release some about the bugs so speculation could stop and facts could finally flow in. The merits or not of disclosure embargos deserve a serious discussion but this post is not the place for it. This one was for sure a huge mess.

The world was finally introduced to. One of the more interesting rumours was about the performance impact of the security features implemented by Linux to workaround these bugs, in particular the mitigations against Meltdown (CVE-2017-5754). My economist and system administrator side got interested into this because there was almost no performance data available.

It is interesting and necessary to measure the patch impact to fully understand its consequences. Yes, security is important, but if my whole business goes down because of the performance hit then the security might have to wait some time. No cash flows, no salaries, no business.

Always keep this in mind. On January 5th Apple that both its Intel and ARM systems (read OS X and iOS) were vulnerable to Meltdown and Spectre, and that patches were already released with High Sierra 10.13.2, iOS 11.2, tvOS 11.2. Regarding Meltdown performance impact they wrote: Our testing with public benchmarks has shown that the changes in the December 2017 updates resulted in no measurable reduction in the performance of macOS and iOS as measured by the GeekBench 4 benchmark, or in common Web browsing benchmarks such as Speedometer, JetStream, and ARES-6. There is a funny story about the patch in High Sierra 10.13.2. My SentinelOne colleague Julien-Pierre detected a crash in our kernel extension with a 10.13.2 beta release. I started looking at the issue and the reason was that we were still using the IDT table to locate the kernel image in memory.

The Meltdown patch introduces changes in IDT to improve the memory separation between kernel and user apps (the Meltdown vulnerability). I didn’t pursue reverse engineering the whole thing but I had a feeling at the time they were trying to separate kernel and user spaces.

I thought Apple was experimenting with some new exploit mitigation strategy. Now with hindsight it is easier to look at the changes:-). Anyway, I decided to make a series of tests to measure the impact of the 10.13.2 patch. Benchmarking is not an easy task and the initial reports of different impacts depending on the workloads doesn’t make the problem easier. So my assumption was to try to measure one extreme - pure system call (syscall hereafter) performance - and some mundane tasks - compiling XNU kernel, unpacking Xcode 9.2, and run GeekBench 4 to reproduce Apple results. The general conclusion points toward a real performance loss with High Sierra 10.13.2.

This result was expected because of the engineering behind the patch and other operating systems initial reports. The syscall results are somewhat atrocious with 2 to 4 times increases in total execution time. This at “face” value looks really bad and some workloads will definitely suffer, hence the need to measure the impact for each specific scenario. But OS X and iOS are mostly desktop type operating systems and so the impact is somewhat smoothed because they aren’t executing millions of syscalls as fast as possible as my tests did. Yes it sucks, we just lost a certain amount of computing power in the space of a week, and for some people there is even real financial impact - some cloud users experienced higher CPU usage which will translate into higher costs.

Some links about performance impacts in other operating systems: - -. At least in OS X there is no much drama with the update, there are trade-offs that system administrators and users need to measure, understand, and accept or reject in cases where it is possible. Setup The tests were made with the following machines: - MacBook Pro 8,2 - 2Ghz 4 Core i7 - 16 GB RAM - Corsair Neutron GTX 240 GB SSD - Mac Pro 6,1 - 3,5 Ghz 6 Core Xeon E5 - 32 GB RAM - 256 GB SSD The MacBook Pro appears on charts as MBP and the Mac Pro as MP. Both have the PCID CPU feature (and XNU uses it in pmappcidconfigure).

If you make the tests in different machines (newer) and the results differ in conclusions please drop me an email with the results. OS X versions used: - Sierra 10.12.6, builds 16G1114 (MacBook Pro), 16G1036 (Mac Pro) - High Sierra 10.13.0 (17A365), 10.13.2 (17C89) The 10.13.2 supplemental update was just released and has a new build number 17C205 but the kernel hasn’t changed versus 17C89. The filesystem was HFS+ in Sierra, encrypted in the Mac Pro and not encrypted in the MacBook Pro, and unencrypted APFS in High Sierra. Other software: - Geekbench 4.2.0 - Xcode 9.2 - Nasm 2.13.02 You can find the source code used for syscall benchmarking. Unless explicit, all the results except Geekbench are total seconds to complete the test. Benchmarks Geekbench 4 Let’s start with the easiest test, Geekbench 4. The results are a bit different between the MacBook and the Mac Pro (their CPUs belong to different classes) but the key takeaway is that the variation amount between the different versions is in practice irrelevant (the run #2 MacBook outlier can be ignored).


The multi-core performance degradation in the Mac Pro can be up to 1% but the initial release of High Sierra improved the score over Sierra, and in the case of the MacBook the results improved from Sierra to High Sierra and 10.13.2 loss isn’t conclusive. These are Geekbench single-core results, five runs on each machine and different OS X versions. Single Core MBP 10.12.6 MBP 10.13.0 MBP 10.13.1 MBP 10.13.2 Run 1 2957 2955 2955 2963 Run 2 2957 2697 2968 2969 Run 3 2955 2948 2977 2963 Run 4 2958 2960 2969 2957 Run 5 2954 2944 2972 2964 Average 2956,2 2900,8 2968,2 2963,2. The MacBook Pro single core performance doesn’t vary much, except for those two outliers, both on 10.13.0.

But the Mac Pro displays a different picture - the single core performance improved from Sierra to High Sierra. Between High Sierra versions there is no conclusive direction but the performance loss is minimal.

The positive variation means that Geekbench scores improved with the new version, and negative that they got worse. Single Core MP 10.12.6 MP 10.13.0 MP 10.13.2 Run 1 3981 3988 3994 Run 2 3969 3993 3972 Run 3 3978 3997 3972 Run 4 3976 3987 3989 Run 5 3963 4000 3996 Average 3973,4 3993 3984,6. The multi-core results are slightly more interesting.

For the MacBook Pro they show a performance improvement from Sierra to High Sierra and minimal performance loss between High Sierra versions. For the Mac Pro the gain is clear from Sierra to High Sierra, and a clear performance loss on 10.13.2 versus Sierra and initial High Sierra (except one case). Still, the performance loss is minimal, up to 1,40%. Multi Core MBP 10.12.6 MBP 10.13.0 MBP 10.13.1 MBP 10.13.2 Run 1 9318 9420 9309 9406 Run 2 9332 9405 9427 9374 Run 3 9365 9426 9418 9426 Run 4 9344 9414 9454 9371 Run 5 9362 9449 9441 9493 Average 9344,2 9422,8 9409,8 9414. The Geekbench results with my reduced machine sample set are in line with Apple’s bulletin - the performance reduction measured by Geekbench is mostly irrelevant (unless you are some Geekbench freak trying to rank the highest score possible).

Now let’s try to assess performance using some mundane daily tasks - compiling XNU kernel, unpacking an archive, and then move to a very specific task, massive syscall execution. Compiling XNU 10.13.0 The first test is to compile the open source XNU kernel available at. The High Sierra version has some weird compilation dependencies so I have used Brandon Azad script available. It automates the installation of everything necessary to build XNU.

The test was essentially to compile and make clean, five times in a row. The command line used to compile with the MacBook Pro was: time make -j4 SDKROOT=macosx ARCHCONFIGS=X8664 KERNELCONFIGS='RELEASE' and for the Mac Pro: time make -j6 SDKROOT=macosx ARCHCONFIGS=X8664 KERNELCONFIGS='RELEASE' This is the table with total seconds to finish compilation for each model. MBP 10.12.6 MBP 10.13.0 MBP 10.13.2 MP 10.12.6 MP 10.13.0 MP 10.13.2 Run 1 270 280 304 175 164 177 Run 2 274 280 286 166 164 168 Run 3 270 279 287 166 164 169 Run 4 270 280 287 166 165 168 Run 5 269 280 287 165 164 168 Average 270,6 279,8 290,2 167,6 164,2 170 The results demonstrate a performance loss with 10.13.2 but also an interesting difference between the two machines. The MacBook build times always got worse with each new version, while in the Mac Pro High Sierra 10.13.0 improved the build times versus Sierra 10.12.6. Result of better multi-core performance observed in Geekbench results? The performance loss compiling XNU exists but it’s still below two digits, 2% to 9%. It should be interesting (and important) to verify what is the result in larger projects, if it’s linear or goes exponentially worse (my expectation is that bigger projects with many source files might suffer because of syscall performance).

What you should expect is slightly longer build times if you upgrade your build system to 10.13.2. Xcode 9.2 unpacking The next test is to expand Xcode 9.2 installation archive. This is a 5.2 GB archive that takes a while to extract and consumes enough CPU resources to start spinning MacBook’s fans. Sounds like a good test to measure CPU and disk performance.

The results are somewhat interesting! The 10.12.6 tests run against HFS+ filesystem, encrypted in the Mac Pro case, while High Sierra all run in unencrypted APFS. Xcode 9.2 XIP MBP 10.12.6 MBP 10.13.0 MBP 10.13.2 MP 10.12.6 MP 10.13.0 MP 10.13.2 Run 1 295 408 459 180 253 275 Run 2 301 413 444 181 254 277 Run 3 299 415 439 182 255 276 Run 4 301 415 440 N/A 254 274 The Mac Pro is around 60% faster than the MacBook Pro, but the performance loss will be similar in both. MBP vs MP 10.12.6 10.13.0 10.13.2 Run 1 61,02% 62,01% 59,91% Run 2 60,13% 61,50% 62,39% Run 3 60,87% 61,45% 62,87%. We can clearly observe this is where the fun starts.

The 10.13.2 syscall is much slower versus previous versions. The fix introduces significant overhead at the syscall interface as expected. High Sierra 10.13.2 is more than 300% slower executing 250m getpid syscalls versus 10.13.0. Another test run executing only 50 million syscalls reveals the same variation - the performance loss is linear. MBP 10.13.0 vs 10.12.6 MBP 10.13.2 vs 10.13.0 MP 10.13.0 vs 10.12.6 MP 10.13.2 vs 10.13.0 Average -0,45% 343,47% -2,72% 380,20% The getpid libsystemkernel.dylib implementation is slightly more complex because it caches the current pid, avoiding the syscall after the caching.

This explains the huge performance difference between the syscall and system library tests. The results present an interesting detail. Somehow Sierra libc 64 bit binary has slightly higher overhead versus everything else. I have no idea why this is happening - the libsystemkernel.dylib assembly code is exactly the same. We are talking about an irrelevant timing difference but it is still curious why both machines show the same pattern.

It could be due to different process startup overhead since the test is very short. The relevant conclusion is that there is no relevant difference between all library the versions since the libc code is all userland after the PID is cached, coherent with the placebo test.

Read Next is the read syscall. The same loss pattern is observed on all tests although the loss is now around 140%.

This time there is no visible improvement from Sierra to High Sierra (the test runs much longer so startup overhead is irrelevant). In 10.13.2 the 32 bits libc binary is faster than the other tests on both machines. MBP 10.13.0 vs 10.12.6 MBP 10.13.2 vs 10.13.0 MP 10.13.0 vs 10.12.6 MP 10.13.2 vs 10.13.0 syscall 2,54% 145,78% 4,02% 146,07% libc 2,38% 150,86% 3,42% 152,07% libc32 2,33% 103,56% 3,47% 109,88%. Fwrite The fwrite results are also in line with fread so I’ll just show the variation between the different versions. They are clearly similar to fread variations. MBP 10.13.0 vs 10.12.6 MBP 10.13.2 vs 10.13.0 MP 10.13.0 vs 10.12.6 MP 10.13.2 vs 10.13.0 libc 11,30% -2,88% 19,86% 0,12% libc32 62,51% -0,84% 72,73% 0,09% lseek For the lseek test I decided to do two SEEKSET per test, one 4 bytes ahead and then reset back to zero.

The pattern holds and 10.13.2 test times are much longer, more than 200%. The difference is smaller in 32 bits because it was already 50% slower than 64 bit and syscall versions. MBP 10.13.0 vs 10.12.6 MBP 10.13.2 vs 10.13.0 MP 10.13.0 vs 10.12.6 MP 10.13.2 vs 10.13.0 syscall -0,86% 236,18% -0,17% 239,02% libc -0,54% 241,88% 0,12% 244,32% libc32 -1,39% 104,85% -0,50% 110,32%. Myson century usb driver for mac. Conclusions Some of the initial rumours regarding Meltdown discussed the performance impact of the Linux workaround to “truly” isolate the user/kernel boundary. After Meltdown was finally disclosed and it was known that OS X already had a patch released, I got curious about the performance impact and so I tried to benchmark two different Mac models and OS X versions. The main question to be answered is if the performance impact exists?

Improvements In Performance For Mac

Yes, it is very clear. That leads to a follow up question. Is it relevant? My tests demonstrate that the syscall interface is definitely much slower in High Sierra 10.13.2. This could lead to some drama, that in most cases, is not justified (I witnessed some minor drama because I released an early chart to see what happened). What my tests appear to point to is that some workloads will be slower but they are probably not relevant unless you are doing millions of iterations.

Jobs In Performance Improvement

Maybe a 10% impact on your build times is not reasonable at all or you don’t even notice it. The most important thing that users and systems administrators need to do is to measure their specific situation. It’s the only way to be sure if this patch is a problem or not, and build their threat case under this new assumption. One thing is sure, this appears to be here to stay in the medium to long term until all hardware is replaced. Yes it sucks very much that we all lost computing performance due to a CPU bug.

Mprovements In Performance For Mac

Blame all CPU manufacturers and designers. Or you can view this from Adam Smith point of view of “there is no such thing as free lunches”.


The computing power increased spectacularly in past decades. Do we really think this was without any trade-offs, in particular security trade-offs? Or maybe those security trade-offs weren’t even known or taken in account? Because hindsight is 20⁄ 20 and now are finding out old papers discussing early ideas on this type of problems and others exposing their knowledge and previous experimentations with similar issues. So the ignorance defence is probably harder to sustain and it was just a trade-off performance vs security.

Money talks, security walks;-). Cheers to all the researchers involved in Meltdown and Spectre, this was top notch security research. If you detect mistakes or significant differences in other models please let me know. I expect the results to hold with other models so I am curious about it. Have fun, fG!