This post is co-written with Mulugeta Mammo and Akash Shankaran from Intel.
Today, we’re excited to announce the availability of Intel Advanced Vector Extensions 512 (AVX-512) technology acceleration on vector search workloads when you run OpenSearch 2.17+ domains with the 4th generation Intel Xeon Intel instances on the Amazon OpenSearch Service. When you run OpenSearch 2.17 domains on C/M/R 7i instances, you can gain up to 51% in vector search performance at no additional cost compared to previous R5 Intel instances.
Increasingly, application builders are using vector search to improve the search quality of their applications. This modern technique involves encoding content into numerical representations (vectors) that can be used to find similarities between content. For instance, it’s used in generative AI applications to match user queries to semantically similar knowledge articles providing context and grounding for generative models to perform tasks. However, vector search is computationally intensive, and higher compute and memory requirements can lead to higher costs than traditional search. Therefore, cost optimization levers are important to achieve a favorable balance of cost vs. benefit.
OpenSearch Service is a managed service for the OpenSearch search and analytics suite, which includes support for vector search. By running your OpenSearch 2.17+ domains on C/M/R 7i instances, you can achieve up to a 51% price-performance gain compared to the past R5 instances on OpenSearch Service. As we discuss in this post, this launch offers improvements to your infrastructure total cost of ownership (TCO) and savings.
Accelerating generative AI applications with vectorization
Let’s understand how these technologies come together through the building of a simple generative AI application. First, you bring vector search online by using machine learning (ML) models to encode your content (such as text, image or audio) into vectors. You then index these vectors into an OpenSearch Service domain, enabling real-time content similarity search that can be scaled to search billions of vectors in milliseconds. These vector searches provide contextually relevant insights, which can be further enriched by AI for hyper-personalization and integrated with generative models to power chatbots.
Vector search use cases extend beyond generative AI applications. Use cases include image to semantic search, and recommendations such as the following real-world use case from Amazon Music. The Amazon Music application uses vectorization to encode 100 million songs into vectors that represent both music tracks and customer preferences. These vectors are then indexed in OpenSearch, which manages over a billion vectors and handles up to 7,100 vector queries per second to analyze user listening behavior and provide real-time recommendations.
The indexing and search processes are computationally intensive, requiring calculations between vectors that are typically represented as 128–2,048 dimensions (numerical values). The Intel Xeon Scalable processors found on the 7th generation Intel instances use Intel AVX-512 to increase the speed and efficiency of vector operations through the following features:
- Data parallel processing – By processing 512 bits (twice the number of its predecessor) of data at once, Intel AVX-512 efficiently uses SIMD (single input multiple data) to run multiple operations simultaneously, which provides significant speed-up
- Pathlength reduction – The speed-up is due to a significant improvement in pathlength, which is a measure of the number of instructions required to perform a unit of work in workloads
- Power performance savings – You can lower power performance costs by processing more data and performing more operations in a shorter amount of time
Benchmarking vector search on OpenSearch
OpenSearch Services R7i Instances with Intel AVX-512 are an excellent choice for OpenSearch vector workloads. They offer a high CPU-to-memory ratio, which further maximizes the compute potential while providing ample memory.
To verify just how much faster the new R7i instances perform, you can run OpenSearch benchmarks firsthand. Using your OpenSearch 2.17 domain, create a k-NN index configured to use either the Lucene or FAISS engine. Use the OpenSearch Benchmark with the public Cohere 10M 768D dataset to replicate the benchmarks published in this post. Replicate these tests using the older R5 instances as the baseline.
In the following sections, we present the benchmarks that demonstrate the 51% price-performance gains between the R7i and the R5 instances.
Lucene engine results
In this post, we define price-performance as the number of documents that can be indexed or search queries executed given a fixed budget ($1), taking into account the instance cost. The following are results of price-performance with the Cohere 10M dataset.
Up to a 44% improvement in price-performance is observed when using the Lucene engine and upgrading from R5 to R7i instances. The difference between the blue and orange bars in the following graphs illustrates the gains contributed by AVX512 acceleration.
FAISS engine results
We also examine results from the same tests performed on k-NN indexes configured on the FAISS engine. Up to 51% price-performance gains is achieved on index performance simply by upgrading from r5 to r7i instances. Again, the difference between the blue and orange bar demonstrates the additional gains contributed by AVX512.
In addition to price-performance gains, search response times also improved by upgrading R5 to R7i instances with AVX512. P90 and P99 latencies were lower by 33% and 38%, respectively.
The FAISS engine has the added benefit of AVX-512 acceleration with FP16 quantized vectors. With FP16 quantization, vectors are compressed to half the size, reducing memory and storage requirements and in turn infrastructure costs. AVX-512 contributes to further price-performance gains.
Conclusion
If you’re looking to modernize search experiences on OpenSearch Service while potentially lowering costs, try out the OpenSearch vector engine on OpenSearch Service C7i, M7i, or R7i instances. Built on 4th Gen Intel Xeon processors, the latest Intel instances provide advanced features like Intel AVX-512 accelerators, improved CPU performance, and higher memory bandwidth than the previous generation, which makes them an excellent choice for optimizing your vector search workloads on OpenSearch Service.
Credits to: Vesa Pehkonen, Noah Staveley, Assane Diop, Naveen Tatikonda
About the Authors
Mulugeta Mammo is a Senior Software Engineer, and currently leads the OpenSearch Optimization team at Intel.
Vamshi Vijay Nakkirtha is a software engineering manager working on the OpenSearch Project and Amazon OpenSearch Service. His primary interests include distributed systems.
Akash Shankaran is a Software Architect and Tech Lead in the Xeon software team at Intel working on OpenSearch. He works on pathfinding opportunities and enabling optimizations within databases, analytics, and data management domains.
Dylan Tong is a Senior Product Manager at Amazon Web Services. He leads the product initiatives for AI and machine learning (ML) on OpenSearch including OpenSearch’s vector database capabilities. Dylan has decades of experience working directly with customers and creating products and solutions in the database, analytics and AI/ML domain. Dylan holds a BSc and MEng degree in Computer Science from Cornell University.
Notices and disclaimers
Performance varies by use, configuration, and other factors. Learn more on the Performance Index website.
Your costs and results may vary.
Intel technologies may require enabled hardware, software, or service activation.
