out-of-memory

As always, a lengthy problem description. We are currently stress testing our product - and we now we face a strange problem. After one to two hours, heap space begins to grow, the application dies sometime later. Profiling the application shows a very large amount of Finalizer objects, filling the heap. Well, we thought "might be the weird finalizer thread to slow" issue and reviewed for reducing the amount of objects that need to be finalized (JNA native handles in this case). Good idea anyway and reduced thousands of new objects... The next tests showed the same pattern, only one hour later

There is a question that I don't understand. Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.90GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available. What is the sentence mean? I have read the source code. But I cann't understand because of my poor ability. The memory size of GPU is 6GB, the result of memory use that I use tfprof analysis is about 14GB. That is beyond the memory size of GPU. The sentence is showing weather tensorflow allocate the memory of CPU or use the good algorithm about the use of

According to many SO answers and this widely cited blog post , a .NET 4.5 application built for 'Any CPU' with the 'prefer 32-bit' option selected will run as a 32-bit process on both 32-bit and 64-bit systems (unlike in .NET 4.0 and earlier). In other words, x86 and AnyCPU with 'prefer 32-bit' selected are equivalent (ignoring whether or not it can run on ARM). However, my tests have shown that on a 64-bit system an 'AnyCPU prefer 32-bit' application (which I confirm runs 32-bit) can allocate more memory than an x86 one. I wrote a .NET 4.5 C# console app that allocates 10MB byte arrays in a

I am loading images into a grid view from a server. For this, I'm using Universal Image loader. I am using the image loader inside the getview() method of the adapter. It takes the URL of the image from arraylist & loads the image from server. Images can be in 100's. I am displaying the grid view in a fragment so for each fragment there is a separate grid view. When I switch between multiple fragments for a long time I no longer get the out of memory error. Suggested solution : Release the unused bitmap. So I am loading the images using third party library. I am just passing the image view in

We have an application that is running on 5 (server) nodes (16 cores, 128 GB Memory each) that loads almost 70 GB data on each machine. This application is distributed and serves concurrent clients, therefore, there is a lot of sockets usage. Similarly, for synchronization between multiple threads, there are a few synchronization techniques being used, mostly using System.Threading.Monitor . Now the problem is that while application is running and the data is traveling between these server nodes and between clients and servers, one or two server machines start receiving OutOfMemoryException

I am geting the above error when i run a model training pipeline with spark `val inputData = spark.read .option("header", true) .option("mode","DROPMALFORMED") .csv(input) .repartition(500) .toDF("b", "c") .withColumn("b", lower(col("b"))) .withColumn("c", lower(col("c"))) .toDF("b", "c") .na.drop()` inputData has about 25 million rows and is about 2gb in size. the model building phase happens like so val tokenizer = new Tokenizer() .setInputCol("c") .setOutputCol("tokens") val cvSpec = new CountVectorizer() .setInputCol("tokens") .setOutputCol("features") .setMinDF(minDF) .setVocabSize

Import of Maven project ends with Exception java.lang.OutOfMemoryError: GC overhead limit exceeded I've tried to define an environment variable: MAVEN_OPTS = -Xmx1g . I've tried to add -Xmx1g to Maven VM options via Settings -> Build Tools -> Maven -> Runner. Without success. What else can I do? The solution is to set -Xmx1g (or more) in Maven VM options for importer (yes, it is a separate option!) . Go to "Settings -> Build Tools -> Maven -> Importing", and find the option there. Read more on JVM heap size: (Q) What are the Xms and Xmx parameters when starting JVMs? . Wangshen Bi if you want