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mSWEEP GPU comparisons

The following abundance estimations were performed starting with efaec-1_1.aln.gz (2.9 GBs) and efaec-1_2.aln.gz (2.8 GBs) pseudoalignment files which can be obtained by following this mGEMS tutorial.

Table 1: algorithm comparisons across HPC platforms in terms of time, iterations, and memory usage.

Notes:

  • On Turso, A100 GPUs were used, although other GPUs are also possible to use but have less memory and will most likely run slower.
  • On LUMI, older versions of LibTorch and ROCm had to be used, most likely affecting the resulting times.
  • Since the emgpu algorthm with the default tolenrace of 1e-6 took all 5000 iterations in this case (rare), some results from running the algorithms with a higher tolerance of 1e-3 are shown. This tolerance still seems to provide nearly identical results but in a faster time (see Table 2 for comparison of results).
  • Time was acquired from the time taken to execute this line.
Platform Algorithm Tolerance Time to Estimate Abundances (seconds) Iterations Max Memory Used (GB)
Turso rcgcpu (8 CPUs) 1.00E-06 1856 205 22.7
Turso rcgcpu (32 CPUs) 1.00E-06 634 215 23.3
Turso rcgcpu (80 CPUs) 1.00E-06 485 215 24.4
Turso rcggpu 1.00E-06 43 220 27.9 (on GPU)
Turso rcggpu 1.00E-03 33 155 27.9 (on GPU)
Turso emgpu (double) 1.00E-06 258 5000 14 (on GPU)
Turso emgpu (double) 1.00E-03 143 2605 14 (on GPU)
Turso emgpu (float) 1.00E-06 19 335 7 (on GPU)
LUMI rcggpu 1.00E-06 103 225 27.9 (on GPU)
LUMI emgpu (double) 1.00E-06 392 5000 14 (on GPU)
LUMI emgpu (float) 1.00E-06 57 300 7 (on GPU)

note emgpu has lower numerical precision and the results will differ from the rcg algorithms.