8/22/2023 0 Comments Wise memory optimizer reddit![]() Assume the following visual representation of an algorithm: If you cannot reduce your complexity, you can still gain a lot of performance if you tweak your algorithm where it really matters, if you can find the right spots. But that is not always possible, let alone easy. The killer is achieve O(1) or quasi- O(1), of course, for instance a HashMap lookup. The best way to improve performance, of course, is by reducing algorithm complexity. You will save wall-clock time, but not reduce complexity! If your algorithm is O(n log n), and you let that algorithm run on c cores, you will still have an O(n log n / c) algorithm, as c is an insignificant constant in your algorithm’s complexity. Parallelism has no effect on your algorithm’s Big O Notation.So parallelism only helps when scaling up. There are good reasons why we’ve used the single-thread servlet model in the past decades. This is great for batch processing, but a nightmare for asynchronous servers (such as HTTP). The advantage of such parallelism compared to scaling across different machines on your network is the fact that you can almost completely eliminate latency effects, as all cores can access the same memory.īut don’t be fooled by the effect that parallelism has! Remember the following two things: Java 7’s ForkJoinPool as well as Java 8’s parallel Stream help parallelising stuff, which is great when you deploy your Java program onto a multi-core processor machine. Anyway, today, we’re going to look at some very easy ways to improve things on the performance side. If there was anything like free lunch ( there isn’t), we could indefinitely combine scaling up and out. This is often also referred to as scaling up You want to do everything possible to keep all calculation on a single machine. Latency is the killer when scaling performance. ![]() ![]() Whether this type of scaling is feasible is best described by Big O Notation. to make sure that an algorithm that works for 1 piece of information will also work well for 10 pieces, or 100 pieces, or millions. This is often also referred to as scaling outĪn entirely different aspect of scaling is about scaling performance, i.e. When load is your problem, latency is probably not, so it’s OK if individual requests take 50-100ms. Ideally, your system is as “stateless” as possible such that the few pieces of state that really remain can be transferred and transformed on any processing unit in your network. to make sure that a system that works for 1 user will also work well for 10 users, or 100 users, or millions. The hype mentioned above is mostly about scaling load, i.e. It is solved (and this applies to any program that does this) by setting the following registry entry REG ADD "HKLM\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Image File Execution Options\utorrent.exe\PerfOptions" /v PagePriority /d 1 /t REG_DWORD /f", if that doesn't work for you (or don't want to mess with this), get another torrent client, like Deluge, QBitTorrent or Transmission-QT.There has been a lot of hype about the buzzword “ web scale“, and people are going through lengths of reorganising their application architecture to get their systems to “scale”.īut what is scaling, and how can we make sure that we can scale? In this particular instance, the RAM use isnt seen under the guilty process. On Windows 10 subreddit, uTorrent also has been pointed out for having a memory leak: "uTorrent since 3.4.2 has had an issue where it allocates file handles over and over and never releases them until its closed and reopened. If the"system" ram hogging keeps going even after upgrading to the latest version, disable Superfetch by following this guide: (note, it says for 8 but applies to 10 as well, disable Superfech only, leave Prefetch alone)ĭo you have a "Killer" network adapter? Go to Registry editor (win + r > regedit) and go to this address: HKEY_LOCAL_MACHINE\SYSTEM\ControlSet001\Services\Ndu, change it's Start value to 4 (for disable). Firefox using almost 1GB for only 2 tabs? either you have too much extensions, bad ones or simply you're visiting a poorly coded site, I have 7 tabs open atm and it's around ~550MB, if you have adblock plus, change it for ublock origin, it will reduce ram usage a lot.Īlso, make sure you're on Windows 10 TH2 which vastly improves memory management (check your version by doing this: win + r, type winver and it should say version 1511 (build 10586.xx))
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