Feature/experiment scaling

[rfhaque]

Draft implementation of the scaling functionality for benchmarks. Currently only strong scaling is implemented. Works for amg2023/openmp
bin/benchpark experiment init --dest ./experiments kripke programming_model=openmp scaling=strong strong-scaling-factor=2 strong-scaling-num-exprs=3

and kripke/openmp
bin/benchpark experiment init --dest ./experiments amg2023 programming_model=openmp experiment=strong strong-scaling-factor=2 strong-scaling-num-exprs=4

[becker33]

We need to implement conditional variants so we can make this clearer. This could be

variant("scaling", default="none", values=("none", "strong", "weak"), description="...")

variant("scaling-factor", default=2, values=lambda x: x >= 2, when="scaling=weak", description="...")
variant("scaling-factor", default=2, values=lambda x: x >= 2, when="scaling=strong", description="...")

variant("scaling-iterations", default=4, values=lambda x: x > 1, when="scaling=weak", description="...")
variant("scaling-iterations", default=4, values=lambda x: x > 1, when="scaling=strong", description="...")

And with a little additional syntax beyond that we could get rid of the duplicate lines.

[becker33]

I would call this ScalingExperiment instead of just Scaling, I think it's a bit clearer that way

[becker33]

I think this method can be simplified slightly

Suggested change

	def generate_strong_scaling_parameters(self, initial_resource_list: list):
	scaling_factor = int(self.spec.variants["strong-scaling-factor"][0])
	num_exprs = int(self.spec.variants["strong-scaling-num-exprs"][0]) - 1
	round_robin_order = self.compute_round_robin_order(initial_resource_list)
	resource_list = [[x] for x in initial_resource_list]
	while num_exprs > 0:
	for idx in round_robin_order:
	for i, r in enumerate(resource_list):
	r.append(r[-1]*scaling_factor if i == idx else r[-1])
	num_exprs=num_exprs-1
	if not num_exprs:
	break
	return resource_list
	def generate_strong_scaling_parameters(self, initial_resource_list: list):
	scaling_factor = int(self.spec.variants["strong-scaling-factor"][0])
	num_exprs = int(self.spec.variants["strong-scaling-num-exprs"][0])
	round_robin_order = self.compute_round_robin_order(initial_resource_list)
	resource_list = [[x] for x in initial_resource_list]
	for i in range(num_exprs - 1):
	idx = (i + round_robin_order[0]) * len(initial_resource_list)
	for r_idx, resource in enumerate(resource_list):
	next_value = resource[-1] * scaling_value if r_idx == idx else resource[-1]
	resource.append(next_value)
	return resource_list

[becker33]

I don't think it's necessarily safe for us to assume that the resource requirements and the problem size are expressed in such neatly compatible ways. I think we should probably assume these could be different dimensions (e.g. resource list is a scalar, problem size is 3D) and round-robin them each separately.

[becker33]

The variant logic can be simplified once we merge #379