How to Fix Outofmemoryerror (Java): Gc Overhead Limit Exceeded in R

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How to Fix Outofmemoryerror (Java): Gc Overhead Limit Exceeded in R

#How to Fix Outofmemoryerror (Java): Gc Overhead Limit Exceeded in R| 来源: 网络整理| 查看: 265

Error java.lang.OutOfMemoryError: GC overhead limit exceeded

This message means that for some reason the garbage collector is taking an excessive amount of time (by default 98% of all CPU time of the process) and recovers very little memory in each run (by default 2% of the heap).

This effectively means that your program stops doing any progress and is busy running only the garbage collection at all time.

To prevent your application from soaking up CPU time without getting anything done, the JVM throws this Error so that you have a chance of diagnosing the problem.

The rare cases where I've seen this happen is where some code was creating tons of temporary objects and tons of weakly-referenced objects in an already very memory-constrained environment.

Check out the Java GC tuning guide, which is available for various Java versions and contains sections about this specific problem:

Java 11 tuning guide has dedicated sections on excessive GC for different garbage collectors:

for the Parallel Collectorfor the Concurrent Mark Sweep (CMS) Collectorthere is no mention of this specific error condition for the Garbage First (G1) collector.Java 8 tuning guide and its Excessive GC sectionJava 6 tuning guide and its Excessive GC section. How to deal with Java Heap Space error while running rjava

You need to modify the java parameters with the following function at the start of your script.

options(java.parameters = "-Xmx8000m")

You may also need restart your environment first.

RStudio: Error in .jarray(m) : java.lang.OutOfMemoryError: Java heap space

The Xmx flag controls the size of the Java runtime heap. You can increase this to a larger value which may allow your R code to run without hitting the ceiling:

> options(java.parameters = "-Xmx4g") # or 8g, or larger than this, ...

Note that this should increase the heap only for the Java process called by your R script. Outside R, whatever heap size was being used by your Java should remain the same. You could also change it externally if you wanted to.

I know about Xmx but did not know how to do this from within the R console. For that, I found this useful blog post:

http://www.bramschoenmakers.nl/en/node/726

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