NVIDIA V100 (Volta) versus NVIDIA A100 (Ampere)

Systems

Results

Minimal Cube

Simple cube of static fluid particles in rest

Minimal case to estimate raw performance of solver core

Two sizes:

• Small: ~7m fluid particles (size of relatively small production case, slightly smaller than what is recommended for four GPU's)
• Big: ~57m fluid particles (size of a bigger production case)

All runs cover 1,000 timesteps

Figure 1.

Dambreak

Collapsing water column under gravity in domain (indicated by lines)

Good case to evaluate performance for tricky particle distributions

Therefore indicates whether load balancing works

Two sizes:

• Small: ~7m fluid particles and ~9m total (slightly smaller than what is recommended for four GPU's)
• Big: ~54m fluid particles and ~64m total (size of a bigger production case)

All runs cover 10000 timesteps

Figure 2.

Altair E-Gearbox

Showcase by Altair for E-Mobility application

Size: ~6.5m fluid particles and ~12m total (slightly smaller than what is recommended for four GPU's)

Run covers 10,000 timesteps

Figure 3.

Aerospace Gearbox

Another showcase for aerospace gearbox applications

Chosen to have another slightly bigger case than previous one to fully benefit from four or more GPU's

Size: ~21m fluid particles and ~26.7m total

Run covers 10,000 timesteps

Figure 4.

Conclusions

Theoretical performance of core is approximately 25 percent faster on A100 (based on Minimal Cube, tendency to more for very large cases). Industrial cases seem to benefit slightly more (up to 30 percent). No noticeable difference regarding scalability.

Additional Notes