Update hnswalg.h

[zeraph6]

Collect_metrics@searchBaseLayer : We should not increment metric_distance_computation with the size of neighbors, since there may be some neighbors already visited.

[yurymalkov]

Hi @zeraph6. Thanks for the PR! I wonder, have you tested if it affects the performance (e.g. due to doing branching on every step)?

[zeraph6]

Hi Malkov,

I tested it and there's no difference for random dataset, and it may probably be the same for skewed dataset.

Ilias

---

From: Yury Malkov @.***> Sent: Sunday, July 11, 2021, 1:46 AM To: nmslib/hnswlib Cc: Ilias AZIZI; Mention Subject: Re: [nmslib/hnswlib] Update hnswalg.h (#324)

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Hi @zeraph6https://github.com/zeraph6. Thanks for the PR! I wonder, have you tested if it affects the performance (e.g. due to doing branching on every step)?

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[zeraph6]

But the difference in the number of distance calculations is important (~5%)

[yurymalkov]

Got it! I'll test it on small dim data and let you know. Also, I wonder, were you using the statistics in the C++ interface?

[zeraph6]

Sure! Yes, I used the statistics in the C++ interface.

Ilias

---

From: Yury Malkov @.> Sent: Sunday, July 11, 2021 6:53:06 PM To: nmslib/hnswlib @.> Cc: Ilias AZIZI @.>; Mention @.> Subject: Re: [nmslib/hnswlib] Update hnswalg.h (#324)

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Got it! I'll test it on small dim data and let you know. Also, I wonder, were you using the statistics in the C++ interface?

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[yurymalkov]

@zeraph6 I've finally tested the change. Sorry for big delay. The change actually leads to a drop of performance for multi-threaded search (up to 2X on low-dim data). This is probably due to more frequent access to a shared variable which causes excessive locking.

There are several solutions for that, but the best is probably to accumulate metrics locally and increment them once search. I wonder, can you update the PR with adding local increments (or a similar solution)?

[yurymalkov]

I've used this code (python that calls C++ code):

import hnswlib
import numpy as np
import pickle
import time

dim = 4

# Generating sample data
for _ in range(10):
    print()
    for num_elements in[10000,100000,1000000]:
        for t in [1,24]:
            
            data = np.float32(np.random.random((num_elements, dim)))
            ids = np.arange(num_elements)

            # Declaring index
            p = hnswlib.Index(space = 'l2', dim = dim) # possible options are l2, cosine or ip

            # Initializing index - the maximum number of elements should be known beforehand
            p.init_index(max_elements = num_elements*2, ef_construction = 200, M = 16)

            # Element insertion (can be called several times):
            p.add_items(data)

            # Controlling the recall by setting ef:
            p.set_ef(50) # ef should always be > k

 
            t0=time.time()

            p.set_num_threads(t)
            labels, distances = p.knn_query(data, k = 1)
            print(f"num_elements: {num_elements:10}, threads:{t:2}, time:{time.time()-t0}")
        print()

For baseline it gives (on a 3900X):

num_elements:      10000, threads:24, time:0.02010512351989746

num_elements:     100000, threads: 1, time:1.32535719871521
num_elements:     100000, threads:24, time:0.21545171737670898

num_elements:    1000000, threads: 1, time:21.12733006477356
num_elements:    1000000, threads:24, time:2.674464464187622

For tested changes it gives:

num_elements:      10000, threads:24, time:0.044780731201171875

num_elements:     100000, threads: 1, time:1.1950428485870361
num_elements:     100000, threads:24, time:0.5131497383117676

num_elements:    1000000, threads: 1, time:21.29676127433777
num_elements:    1000000, threads:24, time:5.72478461265564

Let me know if plan to update the PR (if not I'll do this, but later)

[zeraph6]

Well noted! I will update it asap

Ilias

---

From: Yury Malkov @.> Sent: Monday, August 2, 2021 3:24:49 AM To: nmslib/hnswlib @.> Cc: Ilias AZIZI @.>; Mention @.> Subject: Re: [nmslib/hnswlib] Update hnswalg.h (#324)

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I've used this code (python that calls C++ code):

import hnswlib import numpy as np import pickle import time

dim = 4

Generating sample data

for _ in range(10): print() for num_elements in[10000,100000,1000000]: for t in [1,24]:

        data = np.float32(np.random.random((num_elements, dim)))
        ids = np.arange(num_elements)

        # Declaring index
        p = hnswlib.Index(space = 'l2', dim = dim) # possible options are l2, cosine or ip

        # Initializing index - the maximum number of elements should be known beforehand
        p.init_index(max_elements = num_elements*2, ef_construction = 200, M = 16)

        # Element insertion (can be called several times):
        p.add_items(data)

        # Controlling the recall by setting ef:
        p.set_ef(50) # ef should always be > k


        t0=time.time()

        p.set_num_threads(t)
        labels, distances = p.knn_query(data, k = 1)
        print(f"num_elements: {num_elements:10}, threads:{t:2}, time:{time.time()-t0}")
    print()

For baseline it gives (on a 3900X):

num_elements: 10000, threads:24, time:0.02010512351989746

num_elements: 100000, threads: 1, time:1.32535719871521 num_elements: 100000, threads:24, time:0.21545171737670898

num_elements: 1000000, threads: 1, time:21.12733006477356 num_elements: 1000000, threads:24, time:2.674464464187622

For tested changes it gives:

num_elements: 10000, threads:24, time:0.044780731201171875

num_elements: 100000, threads: 1, time:1.1950428485870361 num_elements: 100000, threads:24, time:0.5131497383117676

num_elements: 1000000, threads: 1, time:21.29676127433777 num_elements: 1000000, threads:24, time:5.72478461265564

Let me know if plan to update the PR (if not I'll do this, but later)

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