Update README.md

ALGO_PARAMS.md

@@ -14,8 +14,8 @@ elements for the queries. Note that in case the algorithm is not be able to find
 is 2-100. Higher ```M``` work better on datasets with high intrinsic dimensionality and/or high recall, while low ```M``` work 
 better for datasets with low intrinsic dimensionality and/or low recalls. The parameter also determines the algorithm's memory 
 consumption, which is roughly ```M * 8-10``` bytes per stored element.  
-As an example for ```d```=4 random vectors optimal ```M``` for search is somewhere around 6, while for high dimensional datasets 
-(word embeddings, good face descriptors), higher ```M``` are required (e.g. ```M```=48, 64) for optimal performance at high recall. 
+As an example for ```dim```=4 random vectors optimal ```M``` for search is somewhere around 6, while for high dimensional datasets 
+(word embeddings, good face descriptors), higher ```M``` are required (e.g. ```M```=48-64) for optimal performance at high recall. 
 The range ```M```=12-48 is ok for the most of the use cases. When ```M``` is changed one has to update the other parameters. 
 Nonetheless, ef and ef_construction parameters can be roughly estimated by assuming that ```M```*```ef_{construction}``` is 
 a constant.

README.md

@@ -203,7 +203,8 @@ https://github.com/dbaranchuk/ivf-hnsw
 * Python implementation (as a part of the clustering code by by Matteo Dell'Amico): https://github.com/matteodellamico/flexible-clustering
 * Java implementation: https://github.com/jelmerk/hnswlib
 * Java bindings using Java Native Access: https://github.com/stepstone-tech/hnswlib-jna
-* .Net implementation:  https://github.com/microsoft/HNSW.Net
+* .Net implementation: https://github.com/microsoft/HNSW.Net
+* CUDA implementation: https://github.com/js1010/cuhnsw
 
 ### Contributing to the repository
 Contributions are highly welcome!