Clean Memory Pc

[Sadoth Royer]

EDIT1: "Force" was in quotes. To be more clear as another poster was, this really only suggests it. You can't really make it happen at a specific point in time. Hence the link to the article on garbage collection in .Net

EDIT 2: I realized everyone here only provided a direct answer to your main question. As for your secondary question. Using Visual Studio 2008 Express will still use the .net framework, which is what performs the garbage collection. So if you ever upgrade to the professional edition, you'll still have the same memory management capabilities/limitations.

Edit 3: This wikipedia aritcles on finalizers gives some good pointers of what is appropriate to do in a finalizer. Basically if you're creating an object that has access to critical resources, or if you're consuming such an object, implement IDispose and/or take advantage of the using statement. Using will automatically call the Dispose method, even when exceptions are thrown. That doesn't mean that you don't need to give the run finalizers hint...

There is a method WaitForPendingFinalizers that will "suspend the current thread until the thread that is processing the queue of finalizers has emptied that queue." Though you shouldn't need to call it.

Hi! how can I clean the memory that blender use? for now the only thing that work for me to clean the memory is to close and open the file, can I find some button or script that know how to clean the memory that blender take?

After I runed too many SAS process and lots of memory was used by SAS, SAS performence will be reduced. May I ask how to manually(or automatically) delete/clean old memory occupied by SAS to increase performence of the rest/current SAS job? (PC SAS)

Sometimes when posters mention memory, they are really running into something else. For example, perhaps many temporary SAS data sets have been filling the WORK area. Additional processing may write to the WORK area, but has to use disk space that is more fragmented, and thus takes longer to utilize.

If you are opening data sets in viewtable or similar and leaving them open then there will be significang memory use as one of the features of Viewtable is that you can EDIT the contents. Which means things have to be kept in memory until viewtable is closed and the result written to disk.

Create subsets if you are looking for specific types of records. I don't manually scroll through any dataset with maybe more than 1000 records (and very seldom that many) as it is generally an inefficient process and often means that I don't quite know why I am actually looking for in the raw data.

I am often double click table with billions of rows

and thousand columns and try quick browsing from top to end.this must a bad habit.I will try to change it.but any solution can manually release the memory occupied by my SAS? ---------delete some temporary files??

Thanks

are you running one macro which iterates over a huge number of images or is the execution of the macro manually started after a user loaded images individually? In any case: would you mind sharing the macro here on the forum so that the developers could actually trace the issue?

Also thank you for the suggestion Bio7 I have used the VisualVM to profile the heap usage whilst running the above script.

I have circled the times at which I ran the above script. You can see that the script quickly starts to fill the heap, despite every image being closed. Looks like a bug potentially when opening .dv files? We have a work-around which is just to close Fiji every now and then, but I think it important to bring to your attention. Please suggest better workaround or fix the bug in next update.

While tuning the garbage collector is possible, it will not address the likely root cause of your problem, which is probably a memory leak. If you have technical aptitude, you could try debugging it using a profiler like JVisualVM as suggested by @Bio7.

Regarding your macro: are you running in batch mode? And on which platform do you run? And which version of Java? There is a known memory leak in Java 8 on OS X when opening and closing lots of windows.

I am running in batch mode (all windows are closed) and I have the same issue when I set batch mode = false.

I have Java 8 and run OS X El Capitan Version 10.11.5

I monitor the memory while I run the script with the built-in Memory monitor in Fiji and no memory gets cleared after closing the last image and every 2nd or 3rd image I reach more than 90% and the Macro stops

At first glance, it looks like the ImagePlus objects (i.e.: ImageJ images) are indeed not being garbage collected. Something is holding references to the image planes. I am digging further in JVisualVM now to learn more.

Memory Clean is the ultimate app for optimizing your Mac's memory and is best used after you have finished using a memory (RAM) intensive app or game. It replicates the feeling of a fresh system restart.

With Memory Clean, you can set the app to automatically clean your memory when free memory falls below a defined threshold amount. You no longer have to remember to keep freeing up your memory to keep your Mac running fast.

Since RAM is short-term data that only works for currently running programs, restarting your device will help clear your RAM. Some applications may be running in the background without your knowledge, and a restart tells those processes to stop and give your RAM a break.

Some browsers are better at allocating and managing RAM usage than others. If you rely heavily on one browser and have noticed it running slowly, it might be time for a more intelligent and secure browser.

Since adjusting for best performance will likely make your computer screen less visually appealing, you can manually turn off certain effects and leave others. Once you free up enough RAM, you can turn them back on as needed.

Each Finder window you open requires RAM to display. Rather than opening several windows at once, you can change your settings to ensure your new folders open as tabs instead of new windows. There are two ways to merge your Finder windows and help conserve RAM.

To see how much RAM you have on a Mac, click the Apple icon on the left side of the taskbar at the top of your screen and click About This Mac on the dropdown. Next to Memory, you will see how much RAM you have.

It can take a lot of time and energy to diagnose your RAM shortage and solve the problem. Thankfully, some programs, like Norton Utilities Ultimate, are built to clear away unnecessary information and unused files automatically, which helps you free up and optimize your RAM.

If you don't want to deal with the hassle of manual maintenance, use dedicated computer optimization software to keep your device in excellent shape. Norton Utilities Ultimate can help speed up and clean up your Windows computer by turning off unneeded background programs, clearing out junk files, and deleting unnecessary software. Enjoy a cleaner, faster machine with Norton Utilities Ultimate.

To tell what kind of RAM you have, navigate to the window you use to see how much RAM you have (About This Mac for Mac and the Memory tab of the Task Manager for Windows). Along with how much RAM you have, you should be able to see what type of RAM you have.

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I have been testing with Ray for the past few months to determine its viability for our company to speed up and scale Python development and processing. I am using Ray as a standalone tool and testing locally as well as on a remote, single-node, cluster (a server).

My question is: what is the expected workflow to take care of this?

Should I be appending all results to a file and then deleting the Python object?

Should I be adding all values to a separate database and then deleting the Python object?

Can Ray[data] play a role to solve this?

I suspect that I have additional memory problems as ray seems to hold a bunch of memory even after the job is completed and ray memory shows no memory being used. The memory is released again only when I stop the ray cluster.

Hmm, deep copying should take it out of scope (specifically you want to .copy() the numpy array here). You can try with a single result and checking that it is no longer in ray memory after copying and un-refing the original object. Another possibility is that the object is not garbage collected yet by Python (you can use gc.collect() to force gc).

I made a change in my code so that as soon as a result is received, it is immediately deepcopied to a local variable and then the local variable is appended to the class attribute. This does break the pinned connection to the object store so all is good! (Although it does add time to my processing).

According to the user manual, Warm Boot RAM and Memory Clear have opposite effects. It is tough particularly with ASUS as usually the manuals are poorly translated to English.

I agree that Memory Clear probably zeroes the memoryso that there are no left-overs in RAM from the previous boot.This is a security measure to ensure that sensitive data,such as password, cannot be retrieved by a virus immediately after the boot terminates.It will of course slow down the boot in proportion to the amount of installed RAM.

Warm Boot RAM [Enabled]

Allows you to enable or disable the re-use of data in the RAM after a warm boot to speed-up the boot process.

Configuration options: [Enabled] [Disabled]

I believe that Memory Clear is "the opposite" as regarding boot-speed,slowing down the boot which Warm Boot RAM is supposed to speed-up.In any case, it destroys the RAM that Warm Boot RAM is supposed not to destroy(although preserving it is useless to anything other than a virus).

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