Try   HackMD

LUMI-G training

We assume that you have already allocated resources with salloc

cp -r /projappl/project_465000320/exercises/AMD/HIP-Examples/ .

Basic examples

cd HIP-Examples/vectorAdd

Examine files here – README, Makefile and vectoradd_hip.cpp Notice that Makefile requires HIP_PATH to be set. Check with module show rocm or echo $HIP_PATH Also, the Makefile builds and runs the code. We’ll do the steps separately. Check also the HIPFLAGS in the Makefile.

make vectoradd_hip.exe
srun -n 1 ./vectoradd_hip.exe

Now let’s try the cuda-stream example. This example is from the original McCalpin code as ported to CUDA by Nvidia. This version has been ported to use HIP. See add4 for another similar stream example.

cd HIP-Examples/cuda-stream
make
srun -n 1 ./stream

Note that it builds with the hipcc compiler. You should get a report of the Copy, Scale, Add, and Triad cases.
Check that we need to declare target GPU for MI250x, is the --offload-arch=gfx90a

Hipify example

We’ll use the same HIP-Examples that were downloaded for the first exercise Get a node allocation.

Exercise 1: Manual code conversion from CUDA to HIP (10 min)

Choose one or more of the CUDA samples in HIP-Examples/mini-nbody/cuda repository and manually convert them to HIP.
Some code suggestions include mini-nbody/cuda/<nbody-block.cu,nbody-orig.cu,nbody-soa.cu>

The CUDA samples are located in HIP-Examples/mini-nbody/cuda

Manually convert the source code of your choice to HIP

You’ll want to compile on the node you’ve been allocated so that hipcc will choose the correct GPU architecture.

Exercise 2: Code conversion from CUDA to HIP using HIPify tools (10 min)

Use the hipify-perl.sh -inplace -print-stats to “hipify” the CUDA samples you used to manually convert to HIP in Exercise 1. hipify-perl.sh is in $ROCM_PATH/hip/bin directory and should be in your path.

[HIPIFY] info: file 'nbody-orig.cu' statistics:
  CONVERTED refs count: 7
  TOTAL lines of code: 91
  WARNINGS: 0
[HIPIFY] info: CONVERTED refs by names:
  cudaFree => hipFree: 1
  cudaMalloc => hipMalloc: 1
  cudaMemcpyDeviceToHost => hipMemcpyDeviceToHost: 1
  cudaMemcpyHostToDevice => hipMemcpyHostToDevice: 1

  • Compile the HIP programs. Fix any compiler issues, for example, if there was something that didn’t hipify correctly. Be on the lookout for hard-coded Nvidia specific things like warp sizes and PTX. For the nbody-orig.cu code, compile with hipcc -DSHMOO -I ../ nbody-orig.cu -o nbody-orig. The #define SHMOO fixes some timer printouts. Add –offload-arch=<gpu_type> to specify the GPU type and avoid the autodetection issues when running on a single GPU on a node.

  • Run the programs.

Profiling Tools

Rocprof

  • Get the exercise:

https://github.com/ROCm-Developer-Tools/HIP-Examples/tree/master/mini-nbody

  • Compile and run the code
cd mini-nbody/hip

Can compile and run all with

./HIP-nbody-orig.sh

Or just run compile and run one case

hipcc -I../ -DSHMOO nbody-orig.cpp -o nbody-orig
./nbody-orig 65536
65536, 161.871
  • Note that we can find the compile line and the executable name by checking the file HIP-nbody-orig.sh or by watching the output from running the script.
cat HIP-nbody-orig.sh

...

EXE=nbody-orig
...
./$EXE 65536
...

  • The binary is called nbody-orig

  • Use rocprof with --stats

rocprof --stats nbody-orig 65536

RPL: on '221130_200946' from '/global/software/rocm/rocm-5.3.0' in '/global/home/gmarko/HIP-Examples/mini-nbody/hip'
RPL: profiling '"nbody-orig" "65536"'
RPL: input file ''
RPL: output dir '/tmp/rpl_data_221130_200946_3670592'
RPL: result dir '/tmp/rpl_data_221130_200946_3670592/input_results_221130_200946'
ROCProfiler: input from "/tmp/rpl_data_221130_200946_3670592/input.xml"
  0 metrics
65536, 159.960

ROCPRofiler: 10 contexts collected, output directory /tmp/rpl_data_221130_200946_3670592/input_results_221130_200946
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.csv' is generating
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.stats.csv' is generating

Files with the prefix results are created

  • Check the files results.csv

You can see information for each kernel call with their duration

 cat results.csv
 
"Index","KernelName","gpu-id","queue-id","queue-index","pid","tid","grd","wgr","lds","scr","arch_vgpr","accum_vgpr","sgpr","wave_size","sig","obj","DispatchNs","BeginNs","EndNs","CompleteNs","DurationNs"
0,"bodyForce(Body*, float, int) [clone .kd]",0,0,0,3670615,3670615,65536,256,0,0,20,4,16,64,"0x0","0x7f7b27c04500",1591372809346673,1591372809872935,1591372836189584,1591372836215944,26316649
1,"bodyForce(Body*, float, int) [clone .kd]",0,0,2,3670615,3670615,65536,256,0,0,20,4,16,64,"0x0","0x7f7b27c04500",1591372836774261,1591372837000949,1591372863116796,1591372863132315,26115847
2,"bodyForce(Body*, float, int) [clone .kd]",0,0,4,3670615,3670615,65536,256,0,0,20,4,16,64,"0x0","0x7f7b27c04500",1591372863652552,1591372863877281,1591372889980009,1591372889994436,26102728
3,"bodyForce(Body*, float, int) [clone .kd]",0,0,6,3670615,3670615,65536,256,0,0,20,4,16,64,"0x0","0x7f7b27c04500",1591372890512133,1591372890735562,1591372916796147,1591372916817087,26060585
4,"bodyForce(Body*, float, int) [clone .kd]",0,0,8,3670615,3670615,65536,256,0,0,20,4,16,64,"0x0","0x7f7b27c04500",1591372917332974,1591372917556629,1591372943652575,1591372943667909,26095946
5,"bodyForce(Body*, float, int) [clone .kd]",0,0,10,3670615,3670615,65536,256,0,0,20,4,16,64,"0x0","0x7f7b27c04500",1591372944181896,1591372944405378,1591372970475883,1591372970491020,26070505
6,"bodyForce(Body*, float, int) [clone .kd]",0,0,12,3670615,3670615,65536,256,0,0,20,4,16,64,"0x0","0x7f7b27c04500",1591372971009527,1591372971233309,1591372997318181,1591372997339821,26084872
7,"bodyForce(Body*, float, int) [clone .kd]",0,0,14,3670615,3670615,65536,256,0,0,20,4,16,64,"0x0","0x7f7b27c04500",1591372997856209,1591372998080743,1591373024164495,1591373024180993,26083752
8,"bodyForce(Body*, float, int) [clone .kd]",0,0,16,3670615,3670615,65536,256,0,0,20,4,16,64,"0x0","0x7f7b27c04500",1591373024701060,1591373024924818,1591373051023611,1591373051040364,26098793
9,"bodyForce(Body*, float, int) [clone .kd]",0,0,18,3670615,3670615,65536,256,0,0,20,4,16,64,"0x0","0x7f7b27c04500",1591373051559851,1591373051782878,1591373077878145,1591373077902255,26095267
  • Check the statistics result file, one line per kernel
cat results.stats.csv
 
"Name","Calls","TotalDurationNs","AverageNs","Percentage"
"bodyForce(Body*, float, int) [clone .kd]",10,261124944,26112494,100.0
  • Profile the HIP calls with --hip-trace
rocprof --stats --hip-trace nbody-orig 65536
RPL: on '221130_201416' from '/global/software/rocm/rocm-5.3.0' in '/global/home/gmarko/HIP-Examples/mini-nbody/hip'
RPL: profiling '"nbody-orig" "65536"'
RPL: input file ''
RPL: output dir '/tmp/rpl_data_221130_201416_3670892'
RPL: result dir '/tmp/rpl_data_221130_201416_3670892/input_results_221130_201416'
ROCTracer (pid=3670915):
    HIP-trace()
65536, 161.051
hsa_copy_deps: 0
scan ops data 29:30                                                                                                    File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.copy_stats.csv' is generating
dump json 19:20
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.json' is generating
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.hip_stats.csv' is generating
dump json 51:52
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.json' is generating
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.stats.csv' is generating
dump json 9:10
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.json' is generating

Now we have new files with the hip in their name like below, check the file results.hip_stats.csv

 cat results.hip_stats.csv
"Name","Calls","TotalDurationNs","AverageNs","Percentage"
"hipMemcpy",20,486845521,24342276,99.89375113830629
"hipLaunchKernel",10,467008,46700,0.09582337501179998
"hipMalloc",1,30570,30570,0.006272527610042495
"hipFree",1,14210,14210,0.0029156891507590398
"__hipPushCallConfiguration",10,3510,351,0.0007202018943817191
"__hipPopCallConfiguration",10,2520,252,0.0005170680267355932
  • Profile also the HSA API with the --hsa-trace
rocprof --stats --hip-trace --hsa-trace nbody-orig 65536
RPL: on '221130_201737' from '/global/software/rocm/rocm-5.3.0' in '/global/home/gmarko/HIP-Examples/mini-nbody/hip'
RPL: profiling '"nbody-orig" "65536"'
RPL: input file ''
RPL: output dir '/tmp/rpl_data_221130_201737_3671219'
RPL: result dir '/tmp/rpl_data_221130_201737_3671219/input_results_221130_201737'
ROCProfiler: input from "/tmp/rpl_data_221130_201737_3671219/input.xml"
  0 metrics
ROCTracer (pid=3671242):
    HSA-trace()
    HSA-activity-trace()
    HIP-trace()
65536, 155.978

ROCPRofiler: 10 contexts collected, output directory /tmp/rpl_data_221130_201737_3671219/input_results_221130_201737
hsa_copy_deps: 1
scan hsa API data 5953:5954                                                                                                    hsa_copy_deps: 0
scan hip API data 51:52                                                                                                    File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.csv' is generating
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.stats.csv' is generating
dump json 9:10
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.json' is generating
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.hsa_stats.csv' is generating
dump json 5963:5964
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.json' is generating
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.copy_stats.csv' is generating
dump json 19:20
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.json' is generating
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.hip_stats.csv' is generating
dump json 51:52
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/results.json' is generating
  • See the content of the file results.hsa_stats.csv
cat results.hsa_stats.csv
"Name","Calls","TotalDurationNs","AverageNs","Percentage"
"hsa_signal_wait_scacquire",50,264955082,5299101,82.69977799679005
"hsa_queue_create",1,39868279,39868279,12.443987854568068
"hsa_amd_memory_async_copy",20,4917141,245857,1.5347751249357586
"hsa_amd_signal_async_handler",20,4262555,213127,1.3304608069344652
"hsa_signal_store_screlease",40,1945998,48649,0.60739956889069
"hsa_amd_memory_lock_to_pool",20,1418202,70910,0.44265990170591873
"hsa_amd_memory_unlock",20,723957,36197,0.22596691758953363
"hsa_agent_get_info",80,671007,8387,0.20943976433821374
"hsa_amd_memory_pool_allocate",5,597926,119585,0.18662917157599068
"hsa_system_get_info",5005,367139,73,0.11459419296574767
"hsa_executable_load_agent_code_object",2,216629,108314,0.0676158768966984
"hsa_executable_freeze",2,156380,78190,0.048810504729771616
"hsa_amd_agents_allow_access",4,89470,22367,0.02792605101785821
"hsa_signal_create",57,51500,903,0.016074568318092074
"hsa_code_object_reader_create_from_memory",2,20940,10470,0.006535950690890253
"hsa_isa_get_info_alt",2,18180,9090,0.005674478680056581
"hsa_signal_load_relaxed",236,17880,75,0.005580840418009442
"hsa_system_get_major_extension_table",3,14920,4973,0.004656942899144344
"hsa_amd_profiling_get_async_copy_time",40,13430,335,0.004191872864310224
"hsa_executable_create_alt",2,9030,4515,0.0028185116876188626
"hsa_amd_memory_pool_get_info",106,7570,71,0.002362805478989456
"hsa_amd_agent_iterate_memory_pools",27,7440,275,0.0023222288987690297
"hsa_amd_memory_pool_free",1,4710,4710,0.0014701207141400712
"hsa_executable_get_symbol_by_name",15,3599,239,0.0011233470170255023
"hsa_queue_add_write_index_screlease",20,3500,175,0.0010924463905499467
"hsa_amd_profiling_get_dispatch_time",20,3110,155,0.0009707166498886669
"hsa_signal_silent_store_relaxed",40,2800,70,0.0008739571124399574
"hsa_amd_agent_memory_pool_get_info",19,2490,131,0.0007771975749912477
"hsa_iterate_agents",2,2250,1125,0.0007022869653535371
"hsa_queue_load_read_index_relaxed",20,2240,112,0.0006991656899519659
"hsa_signal_destroy",20,2190,109,0.0006835593129441095
"hsa_queue_load_read_index_scacquire",20,1790,89,0.0005587082968812585
"hsa_executable_symbol_get_info",30,1540,51,0.00048067641184197657
"hsa_amd_profiling_async_copy_enable",1,440,440,0.00013733611766913615
"hsa_agent_iterate_isas",1,400,400,0.00012485101606285104
"hsa_amd_profiling_set_profiler_enabled",1,140,140,4.3697855621997866e-05
"hsa_dispatch",10,0,0,0.0
  • Download the results.json file on your laptop

From your laptop:
scp username@lumi.csc.fi:/path/results.json .

  • Visit the web page:

https://ui.perfetto.dev/

  • Click on the top left menu, "Open Trace File on th eleft top"

  • Select the file results.json

Zoom in/out: W/S
Move left/right: A/D

Read about the counters: vim /opt/rocm/rocprofiler/lib/gfx_metrics.xml

  • Create a file with the contents:
cat rocprof_counters.txt
pmc : Wavefronts VALUInsts VFetchInsts VWriteInsts VALUUtilization VALUBusy WriteSize
pmc : SALUInsts SFetchInsts LDSInsts FlatLDSInsts GDSInsts SALUBusy FetchSize
pmc : L2CacheHit MemUnitBusy MemUnitStalled WriteUnitStalled ALUStalledByLDS LDSBankConflict
  • Execute with using the counters
 rocprof --timestamp on -i rocprof_counters.txt  nbody-orig 65536
RPL: on '221130_205737' from '/global/software/rocm/rocm-5.3.0' in '/global/home/gmarko/HIP-Examples/mini-nbody/hip'
RPL: profiling '"nbody-orig" "65536"'
RPL: input file 'rocprof_counters.txt'
RPL: output dir '/tmp/rpl_data_221130_205737_3673574'
RPL: result dir '/tmp/rpl_data_221130_205737_3673574/input0_results_221130_205737'
ROCProfiler: input from "/tmp/rpl_data_221130_205737_3673574/input0.xml"
  gpu_index =
  kernel =
  range =
  7 metrics
    Wavefronts, VALUInsts, VFetchInsts, VWriteInsts, VALUUtilization, VALUBusy, WriteSize
65536, 155.389

ROCPRofiler: 10 contexts collected, output directory /tmp/rpl_data_221130_205737_3673574/input0_results_221130_205737
RPL: result dir '/tmp/rpl_data_221130_205737_3673574/input1_results_221130_205737'
ROCProfiler: input from "/tmp/rpl_data_221130_205737_3673574/input1.xml"
  gpu_index =
  kernel =
  range =
  7 metrics
    SALUInsts, SFetchInsts, LDSInsts, FlatLDSInsts, GDSInsts, SALUBusy, FetchSize
65536, 156.996

ROCPRofiler: 10 contexts collected, output directory /tmp/rpl_data_221130_205737_3673574/input1_results_221130_205737
RPL: result dir '/tmp/rpl_data_221130_205737_3673574/input2_results_221130_205737'
ROCProfiler: input from "/tmp/rpl_data_221130_205737_3673574/input2.xml"
  gpu_index =
  kernel =
  range =
  6 metrics
    L2CacheHit, MemUnitBusy, MemUnitStalled, WriteUnitStalled, ALUStalledByLDS, LDSBankConflict
65536, 155.264

ROCPRofiler: 10 contexts collected, output directory /tmp/rpl_data_221130_205737_3673574/input2_results_221130_205737
File '/global/home/gmarko/HIP-Examples/mini-nbody/hip/rocprof_counters.csv' is generating
  • Contents of the rocprof_counters.csv file
cat rocprof_counters.csv
Index,KernelName,gpu-id,queue-id,queue-index,pid,tid,grd,wgr,lds,scr,arch_vgpr,accum_vgpr,sgpr,wave_size,sig,obj,Wavefronts,VALUInsts,VFetchInsts,VWriteInsts,VALUUtilization,VALUBusy,WriteSize,SALUInsts,SFetchInsts,LDSInsts,FlatLDSInsts,GDSInsts,SALUBusy,FetchSize,L2CacheHit,MemUnitBusy,MemUnitStalled,WriteUnitStalled,ALUStalledByLDS,LDSBankConflict,DispatchNs,BeginNs,EndNs,CompleteNs
0,"bodyForce(Body*, float, int) [clone .kd]",0,0,0,3673711,3673711,65536,256,0,0,20,4,16,64,0x0,0x7f2b4d282500,2048.0000000000,1212443.0000000000,12.0000000000,12.0000000000,100.0000000000,68.1476813493,7872.0000000000,131228.5000000000,65553.0000000000,0.0000000000,0.0000000000,0.0000000000,6.3483148000,9429.1875000000,96.5684331443,0.0250344612,0.0044357832,0.0102024550,0.0000000000,0.0000000000,1594244102859719,1594244111978746,1594244138305243,1594244138330792
...

Omnitrace

We have made special builds of the Omnitools, omnitrace and omniperf for use in the exercises

  • Reserve a GPU

  • Load Omnitrace

Declare PATH and LD_LIBRARY_PATH
It is temporarily installed here: /projappl/project_465000320/software/omnitrace/1.7.3
export PATH=/projappl/project_465000320/software/omnitrace/1.7.3/bin:$PATH
export LD_LIBRARY_PATH=/projappl/project_465000320/software/omnitrace/1.7.3/lib:$LD_LIBRARY_PATH

  • Check the various options and their values and also a second command for description

srun -n 1 --gpus 1 omnitrace-avail --categories omnitrace
srun -n 1 --gpus 1 omnitrace-avail --categories omnitrace --brief --description

  • Create an Omnitrace configuration file with description per option

srun -n 1 omnitrace-avail -G omnitrace_all.cfg --all

hipcc --offload-arch=gfx90a -o MatrixTranspose MatrixTranspose.cpp
  • Execute the binary: time srun -n 1 --gpus 1 ./MatrixTranspose and check the duration

Dynamic instrumentation

  • Execute dynamic instrumentation: time srun –n 1 –gpus 1 omnitrace -- ./MatrixTranspose and check the duration
  • Check what the binary calls and gets instrumented: nm --demangle MatrixTranspose | egrep -i ' (t|u) '
  • Available functions to instrument: srun -n 1 --gpus 1 omnitrace -v -1 --simulate --print-available functions -- ./MatrixTranspose
    • the simulate option means that it will not execute the binary

Binary rewriting

  • Binary rewriting: srun -n 1 --gpus 1 omnitrace -v -1 --print-available functions -o matrix.inst -- ./MatrixTranspose

    • We created a new instrumented binary called matrix.inst

  • Executing the new instrumented binary: time srun -n 1 --gpus 1 ./matrix.inst and check the duration

  • See the list of the instrumented GPU calls: cat omnitrace-matrix.inst-output/TIMESTAMP/roctracer.txt

Visualization

  • Copy the perfetto-trace.proto to your laptop, open the web page https://ui.perfetto.dev/ click to open the trace and select the file

Hardware counters

  • See a list of all the counters: srun -n 1 --gpus 1 omnitrace-avail --all
  • Declare in your configuration file: OMNITRACE_ROCM_EVENTS = GPUBusy,Wavefronts,VALUBusy,L2CacheHit,MemUnitBusy
  • Execute: srun -n 1 --gpus 1 ./matrix.inst and copy the perfetto file and visualize

Sampling

Activate in your configuration file OMNITRACE_USE_SAMPLING = true and OMNITRACE_SAMPLING_FREQ = 100, execute and visualize

Kernel timings

  • Open the file omnitrace-binary-output/timestamp/wall_clock.txt (replace binary and timestamp with your information)
  • In order to see the kernels gathered in your configuration file, make sure that OMNITRACE_USE_TIMEMORY = true and OMNITRACE_FLAT_PROFILE = true, execute the code and open again the file omnitrace-binary-output/timestamp/wall_clock.txt

Omniperf

We have made special builds of the Omnitools, omnitrace and omniperf for use in the exercises

  • Load Omniperf:
    export PATH=/projappl/project_465000320/software/omniperf/1.0.6/bin/:$PATH
    export PYTHONPATH=/projappl/project_465000320/software/omniperf/puthon-libs:$PYTHONPATH
    module load cray-python
    module load rocm

  • Reserve a GPU, compile the exercise and execute Omniperf, observe how many times the code is executed

`salloc -p gpu --gpus 1 -t 00:40:00`
cp /path/omniperf/1.0.4/share/sample/vcopy.cpp .
hipcc -o vcopy vcopy.cpp
srun -n 1 --gpus 1 omniperf profile -n vcopy_all -- ./vcopy 1048576 256

  • Run srun -n 1 --gpus 1 omniperf profile -h to see all the options

  • Now is created a workload in the directory workloads with the name vcopy_all (the argument of the -n). So, we can analyze it

 srun -n 1 --gpus 1 omniperf analyze -p workloads/vcopy_all/mi200/ &> vcopy_analyze.txt
  • If you want to only roofline analysis, then execute: srun -n 1 --gpus 1 omniperf profile -n vcopy_all --roof-only -- ./vcopy 1048576 256

There is no need for srun to analyze but we want to avoid everybody to use the login node. Explore the file vcopy_analyze.txt

  • We can select specific IP Blocks, like:
srun -n 1 --gpus 1 omniperf analyze -p workloads/vcopy_all/mi200/ -b 7.1.2

But you need to know the code of the IP Block

  • If you have installed Omniperf on your laptop (no ROCm required for analysis) then you can download the data and execute:
omniperf analyze -p workloads/vcopy_all/mi200/ --gui
  • Open the web page: http://IP:8050/ The IP will be displayed in the output
Last changed by 
 
George Markomanolis
0
820

Read more

Omnitrace by Example

MPI Ghost Exchange Optimization ExamplesChanges Between Example VersionsThis code contains several implementations of the same ghost exchange algorithm at varying stages of optimization:

Jun 6, 2024
LUMI Training April 2024

To simplify the login to LUMI, you can add the following to your .ssh/config file.

Apr 26, 2024
Omnitools: Performance Analysis Tools for AMD GPUs, CRAY User Group Tutorial

Access to LUMI: ssh username@lumi.csc.fi

Apr 16, 2024
Omnitools: Performance Analysis Tools for AMD GPUs, LUMI training Estonia

Access to LUMI: ssh username@lumi.csc.fi

Jan 25, 2024
Read more from George Markomanolis
Published on HackMD