Cherif Jazra
Software Engineer · Palo Alto, California
I have two decades of experience working on complex engineering problems, from real-time embedded wireless systems to large-scale cloud data platforms for Data processing and machine learning. I'm currently focused on GPU-accelerated computing for AI Agent driven data exploration.
I've previously worked at: Palm · Apple · Postmates · C3 AI
The AI revolution is fundamentally changing the work of data scientists and engineers. Powerful AI software agents will soon automate a much larger part of the traditional engineering workflow. As more capable GPU accelerators come to market and the software stack for accelerated query engines matures, we are entering a new era of GPU-accelerated data exploration, one increasingly driven by AI data agents in the enterprise.
- Accelerator hardware breakthroughs and deeper CPU and GPU integration in data centers will help overcome memory and communication bottlenecks, enabling massively parallelized query engines that are orders of magnitude faster.
- A maturing software stack and frameworks like RAPIDS AI will provide the building blocks for distributed, multi-node systems that take full advantage of GPU hardware capability and at a lower total cost of ownership.
- AI-accelerated insights: AI agents orchestrating data pipelines end-to-end will deliver high-quality insights to a much broader audience.
In my writings, I will cover industry and research work being done on GPU-accelerated data systems, and will offer a deep technical dive into the software and hardware stack needed to help bring to life this vision.
This is Part 2 of my series on Accelerated Analytics at GTC 2026, focusing on 3 industry talks and 2 DLI training workshops. Read Part 1: Technical Deep Dives.
Accelerated Analytics for structured and unstructured data had a strong presence at this year’s GTC conference. First in the keynote, CEO Jensen Huang spent a good 20 minutes discussing how...
Prefer a section-by-section breakdown? This keynote is also available as a 3-part series starting with Part 1.
This is Part 3 of a 3-part breakdown of the GTC 2026 keynote. Start with Part 1: Overview & Context or go back to Part 2: Intro, Analytics, CUDA-X &...
This is Part 2 of a 3-part breakdown of the GTC 2026 keynote. Start with Part 1: Overview & Context or jump to Part 3: Vera Rubin Hardware, OpenClaw &...
This is Part 1 of a 3-part breakdown of the GTC 2026 keynote. Jump to Part 2: Intro, Analytics, CUDA-X & Inference or Part 3: Vera Rubin Hardware, OpenClaw &...
One of the central arguments for GPU-accelerated analytics is that GPU hardware is advancing faster than server CPUs. But for analytics workloads, the outcome depends on more than raw compute:...
For analytics workloads that fit in fast memory, the hardware case for GPU is strengthening — but the story is more nuanced than raw compute numbers suggest.
#GTC2026 kicks off next week! 🚀
This is Part 3 of a 3-part series covering GTC 2025: Part 1: Keynote and Main Announcements Part 2: Deep Dive into CUDA Part 3: Exhibit Hall - Hardware and...
This is Part 2 of a 3-part series covering GTC 2025:
I attended NVIDIA’s GTC conference in San Jose this year, from March 16th to 21st, and as expected, it was a lot of fun and a very inspiring experience for...
I attended NVIDIA’s GTC conference in San Jose from March 16-21, 2025. It was a lot of fun and a very inspiring experience for technical professionals seeking insights into accelerated...