Evenly distribute the fractions when allocating from multiple GPUs

[achimnol]

For instance, if we have available GPU shares [0.3, 0.3] for two GPUs and try to allocate 0.4 vGPU, the current FractionAllocMap in the agent allocates [0.3, 0.1] vGPU.
But this may cause problems with GPU applications that assume that all (multiple) GPUs they have access to has identical resources. For such applications, we need to allocate [0.2, 0.2].

More test cases:

[0.3, 0.2, 0.1] allocate 0.1 => [0, 0, 0.1]   # should favor smaller fit to reduce fragmentation
[0.3, 0.2, 0.1] allocate 0.15 => [0, 0.15, 0] # should favor smaller fit but use the largest chunk possible
[0.3, 0.2, 0.1] allocate 0.2 => [0, 0.2, 0]   # should favor smaller fit but use the largest chunk possible
[0.3, 0.2, 0.1] allocate 0.3 => [0.3, 0, 0]
[0.3, 0.2, 0.1] allocate 0.4 => [0.2, 0.2, 0]
[0.3, 0.2, 0.1] allocate 0.5 => [0.3, 0.2, 0] or [0.2, 0.2, 0.1]  # if both possible, i'd prefer the lesser number of GPUs with bigger chunks
[0.3, 0.2, 0.1] allocate 0.6 => [0.3, 0.2, 0.1]
[0.3, 0.2, 0.1] allocate 0.7 => insufficient
[0.3, 0.3] allocate 0.3 => [0.3, 0]
[0.3, 0.3] allocate 0.4 => [0.2, 0.2]
[0.3, 0.3] allocate 0.5 => [0.25, 0.25]
[0.3, 0.3] allocate 0.6 => [0.3, 0.3]
[0.3, 0.3] allocate 0.7 => insufficient
[0.2, 0.2, 0.2] allocate 0.2 => [0.2, 0, 0]
[0.2, 0.2, 0.2] allocate 0.3 => [0.15, 0.15, 0]
[0.2, 0.2, 0.2] allocate 0.4 => [0.2, 0.2, 0]
[0.2, 0.2, 0.2] allocate 0.5 => [0.17, 0.17, 0.16]
[0.2, 0.2, 0.2] allocate 0.6 => [0.2, 0.2, 0.2]
[0.2, 0.2, 0.2] allocate 0.7 => insufficient

We also need to limit allocation of too small fractions which results in under ~500 MiB GPU memory (this should be configurable though) since it would not be able to execute anything with some deep learning frameworks due to the framework's default GPU memory footprint (e.g., PyTorch and TensorFlow).

Internal ticket: OP#706

[achimnol]

We have to apply the new allocator by adding alloctator options in the manager-side.

[achimnol]

We also need to expand this allocator to the manager side for distributed multi-container sessions (lablup/backend.ai-manager#217)

[achimnol]

Now this became the default in Backend.AI v20.09 with an addition of quantum_size configuration to limit fragmentation.
We will revisit the allocator to reduce fragmentation further.