View Multi-Core or Mlti-Cpu Utlization on Linux

view multi-core or mlti-cpu utlization on linux

When runnging the top command, press f then j to display the P column (last CPU used by process), in addition to the 1 command in top, you should view some multi core occupation informations :)

Total CPU usage - multicore system

We usually see 100% cpu per core.
I guess there are at least 3 cores/cpus.

try this to count cores:

grep processor /proc/cpuinfo | wc -l

299% is the total cpu usage.

sar and mpstat are often used to display cpu usage of a system. Check that systat package is installed and display total cpu usage with:

$ mpstat 1 1
Linux 2.6.32-5-amd64 (debian)   05/01/2016      _x86_64_        (8 CPU)

07:48:51 PM  CPU    %usr   %nice    %sys %iowait    %irq   %soft  %steal  %guest   %idle
07:48:52 PM  all    0.12    0.00    0.50    0.00    0.00    0.00    0.00    0.00   99.38
Average:     all    0.12    0.00    0.50    0.00    0.00    0.00    0.00    0.00   99.38

If you agree that CPU utilisation is (100 - %IDLE):

$ mpstat 1 1 | awk '/^Average/ {print 100-$NF,"%"}'
0.52 %

view multi-core or mlti-cpu utlization on linux

When runnging the top command, press f then j to display the P column (last CPU used by process), in addition to the 1 command in top, you should view some multi core occupation informations :)

How to check CPU core usage in Linux or R

You are probably looking for htop

or as an alternative, try

mpstat -P ALL 1

Is having multiple cores in a CPU for running multiple threads/processes at once, or for instruction-level parallelism?

ILP is purely within each physical core separately.

cross-site duplicate: How does a single thread run on multiple cores?

(It doesn't - each core has multiple execution units and a wide front-end.
Read my linked answer for details that I'm not going to duplicate. See also Modern Microprocessors
A 90-Minute Guide!)

Also, real CPU cores can pretend to be multiple "logical cores" (i.e. each having register context and can call __schedule() independently). Generically, this is SMT; the mostly widely-known brand-name implementation of that concept is Intel's HyperThreading. Letting multiple software threads share a CPU truly simultaneously (not via software context-switching) gives the core two instruction-streams to find parallelism between as well as within, generally increasing overall throughput (at the cost of single-thread performance to some degree, depending on how busy a single thread of your workload could keep a core).

---

In some contexts, "CPU" is synonym for a single core. e.g. perf stat output saying "7.5 CPUs utilized".

But more often, a CPU refers to the whole physical package, e.g. my CPU is a quad-core, an i7-6700k. Server motherboards are often dual-socket, allowing you to plug in two separate multi-core CPUs.

Perhaps that's what created some terminology confusion?

R: how to check how many cores/CPU usage available

On Linux you can send ps command to the system: it gives you the average cpu usage and the memory usage of the program called rsession:

splitted <- strsplit(system("ps -C rsession -o %cpu,%mem,pid,cmd", intern = TRUE), " ")
df <- do.call(rbind, lapply(splitted[-1], 
                            function(x) data.frame(
                                cpu = as.numeric(x[2]),
                                mem = as.numeric(x[4]),
                                pid = as.numeric(x[5]),
                                cmd = paste(x[-c(1:5)], collapse = " "))))
df
#  cpu mem   pid   cmd
#1 0.8 0.7 11001  /usr/lib/rstudio/bin/rsession 
#2 0.0 0.2 12397  /usr/lib/rstudio/bin/rsession
#3 0.1 0.7 14960  /usr/lib/rstudio/bin/rsession
#4 0.4 0.2 26122  /usr/lib/rstudio-server/bin/rsession 
#5 0.3 8.3 35782  /usr/lib/rstudio/bin/rsession

You can probably improve it to get the parent id and the instantaneous CPU usage with other options passed to ps or top and deduce the number of cores used by each session.

On Windows you can try this:

a <- system("wmic path Win32_PerfFormattedData_PerfProc_Process get Name,PercentProcessorTime", intern = TRUE)
df <- do.call(rbind, lapply(strsplit(a, " "), function(x) {x <- x[x != ""];data.frame(process = x[1], cpu = x[2])}))
df[grepl("Rgui|rstudio", df$process),]
#     process cpu
# 105    Rgui   0
# 108 rstudio   0

Multi-core CPU utilization by Java application

I would suggest, that this is rather a nmon than a java question and to solve it, I would take a peek at the top command which provides info about cpu-usage per process. I predict the following result: You will see one java thread using near 100% cpu-time (which is ok, as per-process percentage in top is relative to one (virtual) core), maybe a second and third java thread with much less cpu-usage (the I/O threads). Depending on the choice of the gc you might even spot one or more gc-Threads, however much less than 20.

HotSpot however will not (and even cannot to my knowledge) parallelize a sequential task on its own.

---

Related Topics