I’m working on a project with Florian that involves using nengo solvers and a *lot* of sample points. When we start getting up around a million sample points, memory usage seems to explode. For example:

```
import nengo
import numpy as np
N = 750
D1 = 16
D2 = 2
S = 1000000
pts = np.zeros((S, D1))
target = np.zeros((S, D2))
pts[:,0] = np.sin(np.arange(S)*0.001)
target[:,0] = np.sin(np.arange(S)*0.001)
model = nengo.Network()
with model:
ens = nengo.Ensemble(n_neurons=N, dimensions=D1)
result = nengo.Node(None, size_in=D2)
c = nengo.Connection(ens, result, eval_points=pts, function=target)
sim = nengo.Simulator(model)
```

Any suggestions? Is there an alternate solver that would be better in this situation?