Uploaded December 2025 | Updated September 2026, 2 weeks ago
Sparse BLAS: Developing a C++ Interface by Ben Brock
PreTalx: pretalx.com/juliacon-2025/talk/AZVLYD
Sparse linear algebra libraries are an important tool for sparse application developers and practitioners. Currently, most applications are implemented directly using vendor-specific libraries such as cuSPARSE and MKL. While vendor libraries provide high performance, they require users to write separate implementations to run on different platforms. In this talk, I will discuss recent efforts in developing a Sparse BLAS interface in C++ as well as our efforts to develop a reference implementation. I will discuss both design decisions that allow vendor-optimized backends to apply optimizations as well as efforts developing a high-level C++ reference implementation using the ranges library, to build patterns for structured iteration.
Sparse BLAS: Developing a C++ Interface by Ben Brock
PreTalx: pretalx.com/juliacon-2025/talk/AZVLYD
Sparse linear algebra libraries are an important tool for sparse application developers and practitioners. Currently, most applications are implemented directly using vendor-specific libraries such as cuSPARSE and MKL. While vendor libraries provide high performance, they require users to write separate implementations to run on different platforms. In this talk, I will discuss recent efforts in developing a Sparse BLAS interface in C++ as well as our efforts to develop a reference implementation. I will discuss both design decisions that allow vendor-optimized backends to apply optimizations as well as efforts developing a high-level C++ reference implementation using the ranges library, to build patterns for structured iteration.



 was developed in [the Robotic Exploration Lab](https://rexlab.ri.cmu.edu/) at Carnegie Mellon University as part of the doctoral dissertation of Kevin Tracy. This derivative-free trajectory optimization method leverages the ability of computers to massively sample trajectories of dynamical systems and transform that data into a quadratic program at each optimization step. This is ongoing research and a paper is currently under review.
[TrajectoryBundles.jl](https://github.com/aarontrowbridge/TrajectoryBundles.jl) utilizes the rich ecosystem for solving differential equations, in particular, the package DiffEqGPU.jl to solve ODEs in parallel on GPUs massively. We also leverage Convex.jl and Clarabel.jl to solve a QP at each iteration. Given recent announcements from Nvidia e.g. [cuDSS](https://developer.nvidia.com/cudss) we are very excited to leverage massively parallel computing in even more facets of this approach. TrajectoryBundles.jl: parallelizable, derivative-free traj. opt. | Trowbridge](https://i.ytimg.com/vi/uHSyZZuz5Wg/mqdefault.jpg)





. This talk focuses on the enhancement of the initially purely deterministic voice control application (see the [paper](https://proceedings.juliacon.org/papers/10.21105/jcon.00121) [1], the [2022 talk](https://www.youtube.com/watch?v=W7oQb7pLc04with) [2], and the [2023 talk](https://www.youtube.com/watch?v gpH-mkrdGM) [3]) with LLM technology.
[JustSayIt.jl](https://github.com/omlins/JustSayIt.jl) can operate entirely offline, utilizing locally installed LLMs via Ollama, with model management and interaction handled through Python packages such as ollama, accessed via PyCall. LLM interactions are streamlined by incorporating dynamic context such as selected text and clipboard content. This enables, for example, near-instantaneous text summaries or translations via single-word voice commands. More complex tasks are enabled by directly forwarding free speech to the LLM, and the LLM output can be instantly converted to audio output using a text-to-speech engine. As a result, almost human-like interaction with the LLM is possible.
For speech recognition, [JustSayIt.jl](https://github.com/omlins/JustSayIt.jl) employs a dual approach: lightweight, low-latency constrained speech recognition using Vosk for deterministic commands and high-accuracy free speech recognition via a faster reimplementation of OpenAIs Whisper for natural language interactions. The system automatically switches between these engines to optimize responsiveness and accuracy, ensuring seamless voice control and LLM interaction without performance bottlenecks.
The approach presented here bridges a crucial gap in human-AI interaction by enabling the seamless integration of LLM intelligence into small, everyday tasks where traditional LLM-chat interactions are overly cumbersome and time-consuming. By combining deterministic, low-latency voice control with LLM capabilities and leveraging offline execution of distilled LLM models, this solution ensures privacy, efficiency, and performance. With limitless customizability and programmability, [JustSayIt.jl](https://github.com/omlins/JustSayIt.jl) empowers users to create their ideal, AI-powered voice assistant tailored to their individual needs.
References:
[1] Omlin, S. (2024). JustSayIt.jl: A Fresh Approach to Open Source Voice Assistant Development. The Proceedings of the JuliaCon Conferences, 6(66), 121, https://doi.org/10.21105/jcon.00121
[2] Omlin, S. (2022). JustSayIt.jl: A Fresh Approach to Open Source Voice Assistant Development. JuliaCon 2022 conference, 2022. https://www.youtube.com/watch?v=W7oQb7pLc04
[3] Omlin, S. (2023). Quick Assembly of Personalized Voice Assistants with JustSayIt. JuliaCon 2023 conference, 2023. https://www.youtube.com/watch?v gpH-mkrdGM Enhancing Deterministic Voice Control with LLM Interaction | Omlin | JuliaCon Global 2025](https://i.ytimg.com/vi/vXLIbWyvIGo/mqdefault.jpg)
