#gpu-acceleration

#gpu-acceleration

The first step in Uber's adoption of OpenSearch was to evaluate it against their existing Lucene-based setup using th HNSW (Hierarchical Navigable Small World) algorithm: We found ourselves limited by the lack of algorithm options, which hindered our ability to fine-tune trade-offs for different scenarios.

The TornadoVM project recently reached version 2.0, a major milestone for the open-source project that aims to provide a heterogeneous hardware runtime for Java. This release is likely to be of particular interest to teams developing LLM solutions on the JVM. The project automatically accelerates Java programs on multi-core CPUs, GPUs, and FPGAs. It does not replace existing JVMs, but instead adds the capability of offloading Java code to the backends,

Lots of companies are announcing AI this and AI that, but few of them offer more than new AI lipstick on an old pig when you look at them closely. Then, there's what SUSE is doing with its release of SUSE Linux Enterprise Server 16 (SLES 16), available today. This new version is positioned as an AI-ready operating system tailored to the demands of today's hybrid cloud, data center, and edge computing environments.

The TornadoVM programming guide demonstrates how developers can utilize hardware-agnostic APIs, enabling the same Java source code to run identically on various hardware accelerators.

LiteRT, previously TensorFlow Lite, enhances on-device ML inference by simplifying GPU and NPU integration, achieving up to 25x speed improvements and lower power usage.