Aggregate different scenes with DAE #79
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This is currently not using the GDALBufferBand but just plain cat.
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Unfortunately the processing of the data doesn't finish when I aggregate the different scenes with DAE. ┌ Warning: There are still cache misses
└ @ YAXArrays.DAT /mnt/felix1/worldmap/dev/YAXArrays/src/DAT/DAT.jl:1099
Progress: 99%|██████████████████████████████████████████████████████▌| ETA: 0:00:10^C^CERROR: InterruptException:
Stacktrace:
[1] try_yieldto(undo::typeof(Base.ensure_rescheduled))
@ Base ./task.jl:958
[2] wait()
@ Base ./task.jl:1022
[3] wait(c::Base.GenericCondition{Base.Threads.SpinLock}; first::Bool)
@ Base ./condition.jl:130
[4] wait
@ ./condition.jl:125 [inlined]
[5] _wait(t::Task)
@ Base ./task.jl:328
[6] wait(t::Task)
@ Base ./task.jl:368
[7] fetch
@ ./task.jl:390 [inlined]
[8] (::Base.var"#1170#1172")(x::Task)
@ Base ./asyncmap.jl:171
[9] foreach(f::Base.var"#1170#1172", itr::Vector{Any})
@ Base ./abstractarray.jl:3187
[10] maptwice(wrapped_f::Function, chnl::Channel{Any}, worker_tasks::Vector{Any}, c::DiskArrays.GridChunks{2, Tuple{…}})
@ Base ./asyncmap.jl:171
[11] wrap_n_exec_twice
@ ./asyncmap.jl:147 [inlined]
[12] #async_usemap#1155
@ ./asyncmap.jl:97 [inlined]
[13] async_usemap
@ ./asyncmap.jl:78 [inlined]
[14] #asyncmap#1154
@ ./asyncmap.jl:75 [inlined]
[15] asyncmap
@ ./asyncmap.jl:74 [inlined]
[16] pmap(f::Function, p::Distributed.CachingPool, c::DiskArrays.GridChunks{…}; distributed::Bool, batch_size::Int64, on_error::Nothing, retry_delays::Vector{…}, retry_check::Nothing)
@ Distributed /mnt/felix1/.julia/juliaup/julia-1.11.3+0.x64.linux.gnu/share/julia/stdlib/v1.11/Distributed/src/pmap.jl:126
[17] pmap(f::Function, p::Distributed.CachingPool, c::DiskArrays.GridChunks{2, Tuple{…}})
@ Distributed /mnt/felix1/.julia/juliaup/julia-1.11.3+0.x64.linux.gnu/share/julia/stdlib/v1.11/Distributed/src/pmap.jl:99
[18] macro expansion
@ ~/.julia/packages/ProgressMeter/kVZZH/src/ProgressMeter.jl:1049 [inlined]
[19] macro expansion
@ ./task.jl:498 [inlined]
[20] macro expansion
@ ~/.julia/packages/ProgressMeter/kVZZH/src/ProgressMeter.jl:1048 [inlined]
[21] macro expansion
@ ./task.jl:498 [inlined]
[22] progress_map(::Function, ::Vararg{…}; mapfun::typeof(Distributed.pmap), progress::ProgressMeter.Progress, channel_bufflen::Int64, kwargs::@Kwargs{})
@ ProgressMeter ~/.julia/packages/ProgressMeter/kVZZH/src/ProgressMeter.jl:1041
[23] progress_pmap(::Function, ::Vararg{Any}; kwargs::@Kwargs{})
@ ProgressMeter ~/.julia/packages/ProgressMeter/kVZZH/src/ProgressMeter.jl:1066
[24] runLoop(dc::YAXArrays.DAT.DATConfig{1, 1}, showprog::Bool)
@ YAXArrays.DAT /mnt/felix1/worldmap/dev/YAXArrays/src/DAT/DAT.jl:711
[25] mapCube(fu::typeof(rqatrend), cdata::Tuple{…}, addargs::Float64; max_cache::Float64, indims::InDims, outdims::OutDims, inplace::Bool, ispar::Bool, debug::Bool, include_loopvars::Bool, showprog::Bool, irregular_loopranges::Bool, nthreads::Dict{…}, loopchunksize::Dict{…}, do_gc::Bool, kwargs::@Kwargs{})
@ YAXArrays.DAT /mnt/felix1/worldmap/dev/YAXArrays/src/DAT/DAT.jl:498
[26] mapCube
@ /mnt/felix1/worldmap/dev/YAXArrays/src/DAT/DAT.jl:311 [inlined]
[27] #rqatrend#29
@ /mnt/felix1/worldmap/dev/RQADeforestation/src/rqatrend.jl:12 [inlined]
[28] macro expansion
@ ./timing.jl:581 [inlined]
[29] main(; tiles::Vector{…}, continent::String, indir::String, outdir::String, years::Vector{…}, polarisation::String, orbit::String, threshold::Float64, folders::Vector{…})
@ RQADeforestation /mnt/felix1/worldmap/dev/RQADeforestation/src/main.jl:110
[30] top-level scope
@ REPL[14]:1
Some type information was truncated. Use `show(err)` to see complete types. |
This is currently not using the GDALBufferBand but just plain cat.
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This is currently not using the GDALBufferBand but just plain cat. I would like to test this with actual large data to see, how the performance is, but unfortunately I currently don't have access to the data at EODC.
We could also improve the performance by not loading the data using Cube but with ArchGDAL directly, but at the moment I think that this should not be a bottleneck, because that is only 1 second out of a few minutes. But we need a proper benchmark of the IO times with this approach to then see what we need to further optimize.