[ Reference : https://yunmingzhang.wordpress.com/2017/10/24/performance-counters-for-measuring-numa-ops/ ]

Some useful performance counters from ocperf measuring loads to remote and local DRAM, cache hits, and DRAM QPI traffic.

To get PMU tools, clone this directory

https://github.com/andikleen/pmu-tools

pmu-tools/ocperf.py stat -e

mem_load_uops_l3_hit_retired.xsnp_hit,

mem_load_uops_l3_hit_retired.xsnp_hitm,

mem_load_uops_l3_hit_retired.xsnp_none,

mem_load_uops_l3_miss_retired.remote_dram,

mem_load_uops_l3_miss_retired.remote_fwd,

mem_load_uops_l3_miss_retired.local_dram -I500 ./executable

This measures l3 cache hits, local DRAM and remote DRAM accesses (ocperf.py stat -e ). The operations are sampled at 500ms intervals with ( -I )

More documentation on the specifics

mem_load_uops_l3_hit_retired.xsnp_hit measures hits that come from cross core snoop (hit in the L2 of another core, could be because the cache line is dirty, the other cores currently owns it) 

mem_load_uops_l3_hit_retired.xsnp_hitm measures hits that come from the shared L3 directly (no snoop involved)

In general, if we are only doing reads, then we should mostly seeing direct read from L3 shared.

Official documentation in pmu-tools ocperf

  mem_load_uops_l3_hit_retired.xsnp_hit      

     Retired load uops which data sources were L3 and cross-core snoop

     hits in on-pkg core cache. (Supports PEBS) Errata: HSM26, HSM30

  mem_load_uops_l3_hit_retired.xsnp_hitm     

     Retired load uops which data sources were HitM responses from

     shared L3. (Supports PEBS) Errata: HSM26, HSM30

  mem_load_uops_l3_hit_retired.xsnp_miss     

     Retired load uops which data sources were L3 hit and cross-core

     snoop missed in on-pkg core cache. (Supports PEBS) Errata: HSM26,

     HSM30

  mem_load_uops_l3_hit_retired.xsnp_none     

     Retired load uops which data sources were hits in L3 without

     snoops required. (Supports PEBS) Errata: HSM26, HSM30

  mem_load_uops_l3_miss_retired.remote_fwd   

     Retired load uop whose Data Source was: forwarded from remote

     cache (Supports PEBS) Errata: HSM30

  mem_load_uops_l3_miss_retired.remote_hitm 

     Retired load uop whose Data Source was: Remote cache HITM

If we want to measure remote LLC reads, we can use offcore counters, such as

offcore_response.all_reads.llc_hit.any_response

To measure QPI traffic

pmu-tools/ucevent/ucevent.py –scale GB QPI_LL.QPI_DATA_BW   — ./executable

Sometimes this would complain about “./”. One way to get around it is to do — taskset -c 0-num_cores ./executable or add something before the “./”.

–scale GB sets the scale to GB, QPI data bandwidth shows the traffic between NUMA nodes.

QPI link goes between the two sockets. There are two in each direction (one for each memory controller) for a total of 4 links. This counter will show the traffic on all 4 links.  On the ones in Lanka, Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz, they are 15 GB/s per link. So max 30 GB/s between the two sockets. This is about 30% less than 40 GB/s local DRAM. This 30GB/s bandwidth therefore limits the remote DRAM bandwidth to 30 GB/s. 

However, a lot of applications are not bandwidth bounded. For latency bounded applications on the current Lanka set up, according to Vlad

local DRAM latency: 80 ns

QPI link latency : 40 ns,

as a result, remote DRAM latency: 80 ns + 40 ns = 120 ns, about 50% slower than local DRAM.

The 40 ns latency QPI link latency also imposes a non-trivial overhead for remote LLC access. A local LLC access is about 34.8-20 ns. So the 40 ns QPI link latency can potentially make a remote LLC as expensive as the local DRAM access.

toplev for measuring everything (see if it is memory bound)

pmu-tools/toplev.py -l2 -m -C2 -S — ./executable

아래 링크에서 "Event not supported" 스레드 참조

출처 : http://www.spinics.net/lists/linux-perf-users/

1. perf : the good, the bad, the ugly

출처 : http://rhaas.blogspot.co.uk/2012/06/perf-good-bad-ugly.html

2. oprofile vs perf

출처 : http://comments.gmane.org/gmane.linux.oprofile/11429

OProfile was, arguably, the profiling tool of choice for Linux developers for nearly 10 years.  A few years
ago, various members of the Linux kernel community defined and implemented a formal kernel API to access
performance monitor counters (PMCs) to address the needs of the performance tools development
community.  Prior to the introduction of this API, oprofile used a special oprofile-specific kernel
module, while other performance tools relied on kernel patches (e.g., 'perfctr', 'perfmon' -- which
were never accepted upstream) to access the PMCs.  The kernel developers of this new API also developed an
example tool that used the new API, which they called 'perf'.  The original perf tool was capable of
profiling (single process or system-wide), as well as simple event counting.  Several
  other features have been added to it since then. The perf tool has matured a lot in the past few years, and has
gained a lot of followers.

Currently, oprofile is strictly a profiling tool.  So there are more features available in the perf tool,
but with those added features, comes added complexity in using it.  Comparing the profiling capabilities
of the two tools, there is a lot of overlap, but they each have their own strengths.  The original oprofile
("legacy" opcontrol-based profiler) could only do system-wide profiling, which required root
authority.  In August 2012, oprofile 0.9.8 was released that included the new 'operf' tool, which uses the
new kernel API mentioned above.  Using operf allows users who know and love oprofile's post-processing
tools to get the same benefits as 'perf' (i.e., single app profiling without the need for root authority),
while still leveraging the advantages of oprofile (symbolic native even
 t names, Java profiling, user manual, and an established community).

In the end, though, which of these  tools to use for profiling is a personal choice.  Try them both out and
decide for yourself.  You may find that you'd like to have them both in your toolbox.