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Articles by Corey Nolet
Build billion-scale vector databases in under an hour with GPU acceleration on Amazon OpenSearch Service
18 hours ago As AI systems continue to advance and take over tasks like writing and even implementing code, many in the tech world believe that traditional coding skills may soon become obsolete. However, Geoffrey Hinton, one of the godfathers of AI, argues that this assumption is premature. He maintains that …
Optimizing Vector Search for Indexing and Real-Time Retrieval with NVIDIA cuVS
AI-powered search demands high-performance indexing, low-latency retrieval, and seamless scalability. NVIDIA cuVS brings GPU-accelerated vector search and clustering to developers and data scientists, enabling faster index builds, real-time updates, and efficient large-scale search for applications like retrieval-augmented generation (RAG), recommendation systems, exploratory data analysis, and anomaly detection.
Even Faster and More Scalable UMAP on the GPU with RAPIDS cuML
UMAP is a popular dimension reduction algorithm used in fields like bioinformatics, NLP topic modeling, and ML preprocessing. It works by creating a k-nearest neighbors (k-NN) graph, which is known in literature as an all-neighbors graph, to build a fuzzy topological representation of the data, which is used to embed high-dimensional data into lower dimensions. RAPIDS cuML already contained an accelerated UMAP, which provided significant speed improvements over the original CPU-based UMAP.
Bringing Confidentiality to Vector Search with Cyborg and RAPIDS cuVS
In the era of generative AI, vector databases have become indispensable for storing and querying high-dimensional data efficiently. However, like all databases, vector databases are vulnerable to a range of attacks, including cyber threats, phishing attempts, and unauthorized access. This vulnerability is particularly concerning considering that these databases often contain sensitive and confidential information.
Accelerating Vector Search: Fine-Tuning GPU Index Algorithms
The first post in this series introduced vector search indexes, explained the role they play in enabling a widespread range of important applications, and provided a brief overview of vector search on the GPU with the RAFT library. In this post, we dive deeper into each of the GPU-accelerated indexes mentioned in Part 1 and give a brief explanation of how the algorithms work, along with a summary of important parameters to fine-tune their behavior.
Accelerating Vector Search: Fine-Tuning GPU Index Algorithms
The first post in this series introduced vector search indexes, explained the role they play in enabling a widespread range of important applications, and provided a brief overview of vector search on the GPU with the RAFT library. In this post, we dive deeper into each of the GPU-accelerated indexes mentioned in Part 1 and give a brief explanation of how the algorithms work, along with a summary of important parameters to fine-tune their behavior.
Accelerating Vector Search: Using GPU-Powered Indexes with RAPIDS RAFT
In the AI landscape of 2023, vector search is one of the hottest topics due to its applications in large language models (LLM) and generative AI. Semantic vector search enables a broad range of important tasks like detecting fraudulent transactions, recommending products to users, using contextual information to augment full-text searches, and finding actors that pose potential security risks.
GPU-Accelerated Single-Cell RNA Analysis with RAPIDS-singlecell
Single-cell sequencing has become one of the most prominent technologies used in biomedical research. Its ability to decipher changes in the transcriptome and epigenome on a cell level has enabled researchers to gain valuable new insights. As a result, single-cell experiments have grown in size and complexity by a factor of over 100, with experiments involving more than 1 million cells becoming increasingly common. However, the resulting data must be analyzed in a highly iterative process.
Reusable Computational Patterns for Machine Learning and Data Analytics with RAPIDS RAFT
In many data analytics and machine learning algorithms, computational bottlenecks tend to come from a small subset of steps that dominate the end-to-end performance. Reusable solutions for these steps often require low-level primitives that are non-trivial and time-consuming to write well. NVIDIA made RAPIDS RAFT to address these bottlenecks and maximize reuse when building algorithms for multidimensional data, such as what is often encountered in machine learning and data analytics.
Faster HDBSCAN Soft Clustering with RAPIDS cuML
HDBSCAN is a state-of-the-art, density-based clustering algorithm that has become popular in domains as varied as topic modeling, genomics, and geospatial analytics. RAPIDS cuML has provided accelerated HDBSCAN since the 21.10 release in October 2021, as detailed in GPU-Accelerated Hierarchical DBSCAN with RAPIDS cuML – Let’s Get Back To The Future. However, support for soft clustering (also known as fuzzy clustering) was not included.
Text Classification with Naive Bayes Algorithms
Naive Bayes (NB) is a simple but powerful probabilistic classification technique that parallelizes well and can scale to datasets of massive size. If you have been working with text processing tasks in data science, you know that machine learning models can take a long time to train. Using GPU-accelerated computing on those models has often resulted in significant gains in time performance, and NB classifiers make no exception.
Accelerating single-cell genomic analysis with GPUs
New Results doi: https://doi.org/10.1101/2022.05.26.493607 Abstract Single-cell genomic technologies are rapidly improving our understanding of cellular heterogeneity in biological systems. In recent years, technological and computational improvements have continuously increased the scale of single-cell experiments, and now allow for millions of cells to be analyzed in a single experiment. However, existing software tools for single-cell analysis do not scale well to such large datasets.
Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence
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Combining Speed & Scale to Accelerate K-Means in RAPIDS cuML
Albert Einstein once said “Imagination is more important than knowledge. Knowledge is limited; imagination encircles the world.” While extracting knowledge is essential for obtaining wisdom in any scientific discipline, it’s gleaning valuable insights from our data that drives us towards understanding. As data sets have grown larger and larger, imagination and intuition for data scientists have been hampered by the realities of compute time.
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