Add the 'btree' benchmark. #381
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AA-Turner
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AA-Turner
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maurycy
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| self.items[p] = extreme | ||
| elif is_big(upper_sibling): | ||
| # Case 2b. | ||
| extreme = upper_sibling.get_min_item() |
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Where is this method defined?
| self.items[p] = extreme | ||
| else: | ||
| # Case 2c. | ||
| extreme = upper_sibling.get_min_item() |
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Where is this method defined?
| # Case 2. | ||
| if is_big(node): | ||
| # Case 2a. | ||
| extreme = node.get_max_item() |
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| for node_id in range(num_nodes): | ||
| a = node_id | ||
| b = f'node {node_id}' | ||
| c = rnd.randbytes(node_id % 100) |
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Introduced in Python 3.9, while:
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This adds a new benchmark that's intended to be a better exercise for the cyclic garbage collector, compared with the existing benchmarks in the suite. The intention is to simulate an application that processes a large number of Python objects in memory, with some relatively complex set of references linking them together. The set of objects should a mixture of GC (container) and non-GC objects since that kind of mixture would be typical in many applications. I initially had the
NUM_NODESparameter set to 3,000,000. However, in order to keep the running time down and reduce memory usage, I decreased it to 200,000.I should note that this benchmark doesn't exercise the GC in terms of breaking reference cycles. The
create_gc_cyclesis kind of a micro benchmark of that. I'd like to add an additional benchmark that more closely simulates an application that creates a bunch of objects but only some of them are cyclic garbage. That would be separate from this PR though.