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qibojit benchmarks on NVIDIA Grace-Hopper
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| #! /usr/bin/bash | |
| # Generates data for qibojit scaling plots | |
| # Command-line parameters | |
| : "${filename:=qibojit_main_breakdown_cpu}" | |
| : "${nreps:=5}" | |
| export CUDA_VISIBLE_DEVICES= | |
| for circuit in qft variational supremacy | |
| do | |
| for precision in single double | |
| do | |
| # these fit within 480GB of CPU memory | |
| for nqubits in 16 18 20 22 24 26 28 30 32 34 | |
| do | |
| python main.py --circuit $circuit --nqubits $nqubits --filename ${filename}.dat \ | |
| --backend=qibojit --platform=numba --nreps $nreps --precision $precision &>> ${filename}.out | |
| echo &>> ${filename}.out | |
| done | |
| done | |
| # these still fit within 480GB of CPU memory | |
| python main.py --circuit $circuit --nqubits 35 --filename ${filename}.dat \ | |
| --backend=qibojit --platform=numba --nreps $nreps --precision single &>> ${filename}.out | |
| echo &>> ${filename}.out | |
| done | |
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| #! /usr/bin/bash | |
| # Generates data for qibojit scaling plots | |
| # Command-line parameters | |
| : "${filename:=qibojit_main_breakdown_gpu}" | |
| : "${nreps:=3}" | |
| export CUDA_VISIBLE_DEVICES=0 | |
| for platform in cupy cuquantum | |
| do | |
| for circuit in qft variational supremacy | |
| do | |
| for nqubits in 16 18 20 22 24 26 28 30 32 | |
| do | |
| # these fit within 96GB of memory | |
| for precision in single double | |
| do | |
| python main.py --circuit $circuit --nqubits $nqubits --filename ${filename}.dat \ | |
| --backend=qibojit --platform=$platform --nreps $nreps --precision $precision &>> ${filename}.out | |
| echo &>> ${filename}.out | |
| done | |
| # this also fits within 96GB of memory | |
| python main.py --circuit $circuit --nqubits $nqubits --filename ${filename}.dat \ | |
| --backend=qibojit --platform=$platform --nreps $nreps --precision single &>> ${filename}.out | |
| echo &>> ${filename}.out | |
| # the following will only run on a GPU with 96GB if managed memory is enabled in the CuPyBackend | |
| python main.py --circuit $circuit --nqubits $nqubits --filename ${filename}.dat \ | |
| --backend=qibojit --platform=$platform --nreps $nreps --precision double &>> ${filename}.out | |
| echo &>> ${filename}.out | |
| # the run above is currently slower than running on CPU, so not running larger circuits | |
| # but if that's improved it could in theory go up to 35 qubits with double precision and 36 with single | |
| done | |
| done | |
| done | |
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| [{"datetime": "2024-02-01 01:01:10", "nqubits": 16, "nreps": 5, "nshots": null, "transfer": false, "numba_threading": null, "gpu_memory": null, "import_time": 9.5367431640625e-07, "backend": "qibojit (numba)", "platform": "numba", "precision": "single", "device": "/CPU:0", "version": "0.2.5", "circuit": "qft", "circuit_options": "nqubits=16, swaps=True", "creation_time": 0.0014972686767578125, "dry_run_time": 0.006263256072998047, "dry_run_transfer_time": 2.384185791015625e-07, "dtype": "complex64", "creation_times": [0.0011997222900390625, 0.0011646747589111328, 0.0011262893676757812, 0.0011289119720458984, 0.0011210441589355469], "simulation_times": [0.0036079883575439453, 0.0035712718963623047, 0.0035772323608398438, 0.003638744354248047, 0.0035238265991210938], "transfer_times": [0.0, 0.0, 0.0, 2.384185791015625e-07, 2.384185791015625e-07], "creation_times_mean": 0.0011481285095214845, "creation_times_std": 3.3598668250132244e-05, "simulation_times_mean": 0.003583812713623047, 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"transfer_times_mean": 4.76837158203125e-07, "transfer_times_std": 2.384185791015625e-07, "measurement_time": 0}] |
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qibojit benchmarks on NVIDIA Grace-Hopper (GH200, 480GB LPDDR5, 96GB HBM3)
This data was obtained with the above variations of
qibojot-benchmarks/scripts/qibojit.sh, modified to usemain.py/circuit_benchmarksinstead ofcompare.py/library_benchmarks(see qiboteam/qibojit-benchmarks#45). This means that comparisons with data from the qibojit paper may not be apples-to-apples...Another difference is that the scaling plots in the paper and here are including the import time, which was about 1s in the data from the paper but negligible in the current system, probably due to faster disk IO and/or caching, so it's better not to include the import time in those comparisons. Removed the import time from the plots in this gist.
The above files include timings for circuits qft, variational and supremacy, both with single and double precision.
Below are some examples for the qft circuit with double precision, plus some comments:
CPU comparisons
GPU comparisons
Digging deeper into Grace-Hopper
(and managed vs unmanaged memory, cupy vs quantum, etc.)
"(numba) cpu" below means using only the Grace CPU, everything else is running the Hopper GPU
"managed memory" below means simply adding
cp.cuda.set_allocator(cp.cuda.MemoryPool(cp.cuda.malloc_managed).malloc)to the constructor ofqibojit'sCuPyBackend(the motivation for this was to oversubscribe the GPU memory, but that is not shown here yet, see the next section)when the circuit still fits within the GPU (96GB of HBM memory), there is a slight overhead when using managed memory during the first run (first plot below) for larger circuits, when qibojit is compiles the circuit. Also, during this stage there is a crossover between the
cuquantumplatform, which is faster for smaller circuits, andcupy, which is faster for larger ones.after the first run, and again while the circuit still fits within the GPU, there is really no observable difference between using managed memory or not (second plot below), and
cupyis consistently faster thancuquantum.when using managed memory, we can add a size that doesn't fit in the GPU memory (33 qubits with double precision requires 128GB of memory); it does run, but slower than on cpu only, I suppose due to the data access pattern, but maybe something can be done about it, in particular for cuquantum by adding support for host state vector migration (https://docs.nvidia.com/cuda/cuquantum/latest/custatevec/host_state_vector_migration.html)
CUDA 12.4 with Access-counter-based migration
CUDA 12.4 "introduces a new memory migration algorithm for NVIDIA Grace Hopper systems. It uses hardware access counters to determine memory page access patterns and migrates memory to the hardware memory (CPU or GPU) from which it’s accessed most often. The result is enhanced data locality for most applications, enabling the use of system-allocated memory (using standard calls like malloc and mmap) from third-party libraries directly in GPU-accelerated kernels." (from https://developer.nvidia.com/blog/cuda-toolkit-12-4-enhances-support-for-nvidia-grace-hopper-and-confidential-computing)
This does improve a bit the performance of the GPU memory oversubscription with cupy managed memory: