Alice Weekly Meeting: Software for Hardware Accelerators / PDP-SRC - MINUTES ONLY

Wednesday 25 Dec 2024, 10:00 → 11:30 Europe/Zurich

1 Discussion

Speakers: David Rohr (CERN), Giulio Eulisse (CERN)

2 Following up JIRA tickets

Speaker: Ernst Hellbar (CERN)

3 TPC ML Clustering

Speaker: Christian Sonnabend (CERN, Heidelberg University (DE))

4 ITS Tracking

Speaker: Matteo Concas (CERN)

5 TPC Track Model Decoding on GPU

Speaker: Gabriele Cimador (Universita e INFN Trieste (IT))

Global Parameter Optimisation

Input dataset simulation

Simulated several timeframes:

• pp: 100kHz, 200kHz, 500kHz, 1MHz, 2MHz
• PbPb: 10kHz, 15kHz, 20kHz, 27kHz, 35kHz, 42kHz, 47kHz, 50kHz

Every timeframe simulated twice, one for 32 orbits timeframe and one for 128 orbits timeframe

For the moment just one simulation per configuration (beam type - interaction rate - timeframe length)

GPU Parameters study

Focusing on grid and block size. Analysed the GPU workflow of the sync/async TPC processing. Image below is the workflow of two HIP streams of the sync TPC processing:

By looking at the tracefile:

• Clusterizer chain:
  • small concurrent kernels
  • overlap during execution
  • overall taking considerable time
  • --> dependent parameters, global optimisation
• SliceTracker chain:
  • medium concurrent kernels
  • all streams used
  • main kernel is TrackletConstructor
  • trace file outputs that CreateSliceData takes a lot of time, however --debug does not say so, still investigating
  • trace file outputs "Marker" which is not present in nvidia trace files, still investigating
  • --> dependent parameters, global optimisation
• Merger chain: 

  • MergeBorders_step2: lots of small concurrent kernels, concurrent to a limited set of other one stream kernels --> dependent parameters, global optimisation (within set)

  • SliceRefit: lots of small one stream kernels --> independent parameters, local optimisation
  • MergerTrackFit: one stream long kernel --> independent parameters, local optimisation (maybe limited since values dependent also on number of tracks)
  • MergerFollowLoopers: one stream medium kernel --> independent parameters, local optimisation
• Compression/Decompression chain:
  • One stream kernels --> independent parameters, local optimisation
  • Multiple stream kernels, not overlapping --> independent parameters, local optimisation

Optimisation strategy

• For the moment just a "Manual Trial-and-Error" using observations from the output
• Started from MergerTrackFit, why:
  • Long kernel
  • One stream
  • Not concurrent to any other kernels
  • Caveat: grid size dependent on number of tracks
• Changing values in GPUDefGPUParameters.h takes a loooong time to compile, even with standalone benchmark
  • Currently forcing custom krnlExec object in kernel calls, e.g.: 
    runKernel<KernelClass, KernelClass::step>({{n_blocks,n_threads,stream}});
  • Not handy, but way faster
• Created script that automatically fetches grid and block size for all the kernels, useful for runtime grid/block numbers like GetGrid(Merger.NOutputTracks(), 0, deviceType)

Possible bug spotted

HIP_AMDGPUTARGET set to "default" in GPU/GPUTracking/Standalone/cmake/config.cmake translates in HIP_AMDGPUTARGET=gfx906;gfx908 and forces to use MI50 params

Basically here HIP_AMDGPUTARGET=gfx906;gfx908 enters the first if clause for MI50 even if I am compiling for MI100. Commented set(HIP_AMDGPUTARGET "default") on the config.cmake of the standalone benchmark and forced usage of MI100 parameters via

cmake -DCMAKE_INSTALL_PREFIX=../ -DHIP_AMDGPUTARGET="gfx908" ~/alice/O2/GPU/GPUTracking/Standalone/

Did not investigate further on this.

6 Efficient Data Structures

Speaker: Dr Oliver Gregor Rietmann (CERN)