memory

问题 I have understood that docker run -m 256m --memory-swap 256m will limit a container so that it can use at most 256 MB of memory and no swap. If it allocates more, then a process in the container (not "the container") will be killed. For example: $ sudo docker run -it --rm -m 256m --memory-swap 256m \ stress --vm 1 --vm-bytes 2000M --vm-hang 0 stress: info: [1] dispatching hogs: 0 cpu, 0 io, 1 vm, 0 hdd stress: FAIL: [1] (415) <-- worker 7 got signal 9 stress: WARN: [1] (417) now reaping child

问题 I have understood that docker run -m 256m --memory-swap 256m will limit a container so that it can use at most 256 MB of memory and no swap. If it allocates more, then a process in the container (not "the container") will be killed. For example: $ sudo docker run -it --rm -m 256m --memory-swap 256m \ stress --vm 1 --vm-bytes 2000M --vm-hang 0 stress: info: [1] dispatching hogs: 0 cpu, 0 io, 1 vm, 0 hdd stress: FAIL: [1] (415) <-- worker 7 got signal 9 stress: WARN: [1] (417) now reaping child

问题 I wanted to compare the performance characteristics of immutable.Map and mutable.Map in Scala for a similar operation (namely, merging many maps into a single one. See this question). I have what appear to be similar implementations for both mutable and immutable maps (see below). As a test, I generated a List containing 1,000,000 single-item Map[Int, Int] and passed this list into the functions I was testing. With sufficient memory, the results were unsurprising: ~1200ms for mutable.Map,

问题 The task is very simple, writting a seqence of integer variable to memory: Original code: for (size_t i=0; i<1000*1000*1000; ++i) { data[i]=i; }; Parallelized code: size_t stepsize=len/N; #pragma omp parallel num_threads(N) { int threadIdx=omp_get_thread_num(); size_t istart=stepsize*threadIdx; size_t iend=threadIdx==N-1?len:istart+stepsize; #pragma simd for (size_t i=istart; i<iend; ++i) x[i]=i; }; The performance sucks, it takes 1.6 sec to writing 1G uint64 variables (which is equal to 5GB

问题 I am looking for a e-LMC extended little man computer program that will accept an indefinite inputs and bubble-sort them. I need to have more inputs continuous and then do the bubble sort. INP //read 1st value STA 0 // for store INP // read 2nd value STA 1 // store INP // read 3rd value STA 2 // store LDA 1 // LOOP 1, STEP 1: SUB 0 // BRP STEP2 // if R0 and R1 are in order, don't swap them LDA 1 // Begin swapping registers STA 3 LDA 0 STA 1 // R1 = R0 LDA 3 STA 0 //R0 = temp STEP2 LDA 2 //

问题 Background: V8 announced a feature called pointer compression (What's happening in V8? - Benedikt Meurer), which is intended to reduce the memory overhead of pointers for 64-bit processes. Java JVM's had a feature called CompressedOops since 2010 (since 6u23). At a first glance, it looks similar but then I realized it is not quite the same. Question: What are the main differences between the pointer compression techniques (V8 vs JVM)? The V8 implementation seems to be still not finalized, but

问题 JavaScript stores Reference Values in Heap memory and thus variables stores to a pointer/address. So, if we change original variable, it changes the assigned one as well: var cols = ['red', 'blue', 'green'] var newCols = cols cols.push('orange') console.log(cols) // returns ["red", "blue", "green", "orange"] console.log(newCols) // returns ["red", "blue", "green", "orange"] However, when I experimenting for reference types, I find out that, when I am using indexing for assignment and push

问题 JavaScript stores Reference Values in Heap memory and thus variables stores to a pointer/address. So, if we change original variable, it changes the assigned one as well: var cols = ['red', 'blue', 'green'] var newCols = cols cols.push('orange') console.log(cols) // returns ["red", "blue", "green", "orange"] console.log(newCols) // returns ["red", "blue", "green", "orange"] However, when I experimenting for reference types, I find out that, when I am using indexing for assignment and push

问题 I know how a GDT (Global Descriptor Table) is implemented and the use of segment registers and segment descriptors. However how/when is a GDT entry accessed? Is it accessed in basic mov instructions like mov [eax],ebx Does this implicitly invoke ds segment register and then access the GDT segment descriptor or there is some other way via which access to the GDT entry happens? 回答1: TL;DR : The Global Descriptor Table (GDT) or Local Descriptor Table (LDT) is only accessed when a segment

问题 I know how a GDT (Global Descriptor Table) is implemented and the use of segment registers and segment descriptors. However how/when is a GDT entry accessed? Is it accessed in basic mov instructions like mov [eax],ebx Does this implicitly invoke ds segment register and then access the GDT segment descriptor or there is some other way via which access to the GDT entry happens? 回答1: TL;DR : The Global Descriptor Table (GDT) or Local Descriptor Table (LDT) is only accessed when a segment