Modern computer systems include cache memory to hide the higher latency and lower bandwidth of RAM memory from the processor. The cache has access latencies ranging from a few processor cycles to ten or twenty cycles rather than the hundreds of cycles needed to access RAM. If the processor must frequently obtain data from the RAM rather than the cache, performance will suffer. With Red Hat Enterprise Linux 6 and newer distributions, the system use of cache can be measured with the perf utility available from the perf RPM.
perf uses the Performance Monitoring Units (PMUs) hardware in modern processors to collect data on hardware events such as cache accesses and cache misses without undue overhead on the system. The PMU hardware is processor implementation specific and the specific underlying events may differ between processors. For example one processor implementation measure the first-level cache events of the cache closest to the processor and another processor implementation may measure lower-level cache events for a cache farther from the processor and closer to main memory. The configuration of the cache may also differ between processors models; one processor in the processor family may have 2MB of last level cache and another member in the same processor family may have 8MB of last level cache. These differences makes direct comparison of event counts between processors difficult.
NOTE: Depending the architecture, the software versions and system configuration use of the PMU hardware by
perfutility may not be supported in KVM guest machines. In Red Hat Enterprise Linux 6, the guest virtual machine does not support the Performance Monitoring Unit hardware.
To get a general understanding of how well or poorly an application is using the system cache, perf can collect statistics on the application and its children using the following command:
$ perf stat -e task-clock,cycles,instructions,cache-references,cache-misses ./stream_c.exe
When the application completes perf will generate a summary of the measurement like the following:
Performance counter stats for './stream_c.exe':
229.872935 task-clock # 0.996 CPUs utilized
626,676,991 cycles # 2.726 GHz
525,543,766 instructions # 0.84 insns per cycle
18,587,219 cache-references # 80.859 M/sec
6,605,955 cache-misses # 35.540 % of all cache refs
0.230761764 seconds time elapsedThe output above is for a simple example program that tests the memory bandwidth performance. The test is relatively short - only 525,543,766 instructions executed requiring 626,676,991 processor cycles. The output also estimates the instructions per processor clock cycle (IPC). The higher IPC the more efficiently the processor is executing instruction on the system. In this case it is 0.84 instructions per clock cycle. The superscalar processor this example is run on could potentially execute four instructions per clock cycle, giving an upper bound of 4 IPC. The IPC will be affected by delay due to cache misses.
The ratio of cache-misses to instructions will give an indication how well the cache is working; the lower the ratio the better. In this example the ratio is 1.26% (6,605,955 cache-misses/525,543,766 instructions). Because of the relatively large difference in cost between the RAM memory and cache access (100's cycles vs <20 cycles) even small improvements of cache miss rate can significantly improve performance. If the cache miss rate per instruction is over 5%, further investigation is required.
It is possible for perf to provide more information about where the cache-misses events occur in code. Ideally, the application code should be compiled with debuginfo (GCC -g option) and debuginfo RPMs installed for the related system supplied executables and libraries, so that perf can map the samples back to the source code and give you a better idea where the cache misses occur.
The perf stat command only provides an overall count of the events. The perf record command performs sampling recording where those hardware events occur. In this case we would like to know where cache misses occur during the execution of the code and we can use the following line to collect that information:
$ perf record -e cache-misses ./stream_c.exe
When the command completes execution output like the following will be printed indicating the amount of data collected (~2093 samples) and stored in a file perf.data.
[ perf record: Woken up 1 times to write data ] [ perf record: Captured and wrote 0.048 MB perf.data (~2093 samples) ]
The perf.data file is analyzed with perf report. By default perf report will put up a simple Text User Interface (TUI) to navigate the collected data. The --stdio option provides simple text output. Below is the "perf report" output for the earlier "perf record". The "perf report" output starts with a header listing when and where the data was collected. The header also includes details about the hardware/software environment and the command used to collect the data.
A sorted list follows the header information and shows which functions the instructions associated with those evens are located in. In this case the simple stream benchmark has over 85% of the samples in main. This is not surprising given this is a simple benchmark that stresses memory performance. However, the second function on the list is the kernel function clear_page_c_e; the [k] indicates this function is in the kernel.
$ perf report --stdio
# ========
# captured on: Wed Oct 2 14:38:59 2013
# hostname : dhcp129-131.rdu.redhat.com
# os release : 3.10.0-31.el7.x86_64
# perf version : 3.10.0-31.el7.x86_64.debug
# arch : x86_64
# nrcpus online : 8
# nrcpus avail : 8
# cpudesc : Intel(R) Core(TM) i7-3740QM CPU @ 2.70GHz
# cpuid : GenuineIntel,6,58,9
# total memory : 7859524 kB
# cmdline : /usr/bin/perf record -e cache-misses ./stream_c.exe
# event : name = cache-misses, type = 0, config = 0x3, config1 = 0x0, config2 =
# HEADER_CPU_TOPOLOGY info available, use -I to display
# HEADER_NUMA_TOPOLOGY info available, use -I to display
# pmu mappings: cpu = 4, software = 1, tracepoint = 2, uncore_cbox_0 = 6, uncore
# ========
#
# Samples: 888 of event 'cache-misses'
# Event count (approx.): 6576993
#
# Overhead Command Shared Object Symbol
# ........ ............ ................. ............................
#
85.19% stream_c.exe stream_c.exe [.] main
11.20% stream_c.exe [kernel.kallsyms] [k] clear_page_c_e
2.23% stream_c.exe stream_c.exe [.] checkSTREAMresults
0.62% stream_c.exe [kernel.kallsyms] [k] _cond_resched
0.20% stream_c.exe [kernel.kallsyms] [k] trigger_load_balance
0.13% stream_c.exe [kernel.kallsyms] [k] task_tick_fair
0.13% stream_c.exe [kernel.kallsyms] [k] free_pages_prepare
0.11% stream_c.exe [kernel.kallsyms] [k] perf_pmu_disable
0.09% stream_c.exe [kernel.kallsyms] [k] tick_do_update_jiffies64
0.09% stream_c.exe [kernel.kallsyms] [k] __acct_update_integrals
0.00% stream_c.exe [kernel.kallsyms] [k] flush_signal_handlers
0.00% stream_c.exe [kernel.kallsyms] [k] perf_event_comm_output
#
# (For a higher level overview, try: perf report --sort comm,dso)
#
You may want more details on which lines of code in main those caches misses are associated with. The perf annotate can provide exactly that information. The left column of the perf annotate output shows the percentage of samples associated with that instruction. The right column shows intermixed source code and assembly language. You can use cursor keys to scroll through the output and 'H' to look at hottest instructions with the most samples. In this case, the addition of elements from two arrays is the hottest area of code.
$ perf annotate
main
│ movsd %xmm0,(%rsp) ▒
│ xchg %ax,%ax ▒
│ #ifdef TUNED ▒
│ tuned_STREAM_Add(); ▒
│ #else ▒
│ #pragma omp parallel for ▒
│ for (j=0; j<N; j++) ▒
│ c[j] = a[j]+b[j]; ▒
1.96 │2a0: movsd 0x24868e0(%rax),%xmm0 ▒
11.17 │ add $0x8,%rax ▒
2.21 │ addsd 0x15444d8(%rax),%xmm0 ▒
13.74 │ movsd %xmm0,0x6020d8(%rax) ▒
│ times[2][k] = mysecond(); ◆
│ #ifdef TUNED ▒
│ tuned_STREAM_Add(); ▒
│ #else ▒
│ #pragma omp parallel for ▒
│ for (j=0; j<N; j++) ▒
3.56 │ cmp $0xf42400,%rax ▒
│ ↑ jne 2a0 ▒
│ c[j] = a[j]+b[j]; ▒
│ #endif ▒
Press 'h' for help on key bindingsTechniques to reduce the cache misses are very dependent on the specifics of the cache organization and the code operation. For more information refer the the software optimization manuals from the processor manufacturers:
AMD:
- Software Optimization Guide for AMD Family 16h Processors, Publication # 52128 Revision: 1.1 Issue Date: March 2013. http://developer.amd.com/wordpress/media/2012/10/SOG_16h_52128_PUB_Rev1_1.pdf
- Software Optimization Guide for AMD Family 15h Processors Publication No. 47414, Revision 3.06, Date January 2012. http://support.amd.com/us/Processor_TechDocs/47414_15h_sw_opt_guide.pdf
- Software Optimization Guide for AMD Family 10h and 12h Processors Publication #40546, Revision: 3.13, Issue Date: February 2011. http://developer.amd.com/wordpress/media/2009/04/40546.pdf
Intel:
- Intel® 64 and IA-32 Architectures Optimization Reference Manual Order Number: 248966-028, July 2013. http://www.intel.com/content/dam/www/public/us/en/documents/manuals/64-ia-32-architectures-optimization-manual.pdf
