Vector and similarity search is increasingly critical in 2023, but most libraries struggle to fully utilize modern hardware due to issues rooted in their code architecture. Many rely on object-oriented programming, which reduces memory-efficiency and data-locality. Additionally, dependence on compilers for low-level optimizations fails to properly emit key AVX-512 and SVE Assembly instructions for x86 and Arm. My talk will dissect these and other pitfalls, and demonstrate how USearch innovates in areas like architecture and SIMD utilization to overcome them.
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Speaker
Ash Vardanian
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talks
Founder
Unum Cloud
Ash Vardanian is the Founder of Unum Cloud. With background across astrophysics, high performance computing, and systems design, Ash focuses on bridging theory and real-world AI applications.
Bio from: December AI, Machine Learning & Data Science Meetup
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Vector and similarity search is increasingly critical in 2023, but most libraries struggle to fully utilize modern hardware due to issues rooted in their code architecture. Many rely on object-oriented programming, which reduces memory-efficiency and data-locality. Additionally, dependence on compilers for low-level optimizations fails to properly emit key AVX-512 and SVE Assembly instructions for x86 and Arm. My talk will dissect these and other pitfalls, and demonstrate how USearch innovates in areas like architecture and SIMD utilization to overcome them.
Apache Lucene was never built for AI-scale vector search. In this talk, I’ll show how USearch bridges the JVM–native gap, pairing Spark’s horizontal scale with native vertical speed.