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.
The 52nd Very Large Data bases conference (VLDB 2026) is just around the corner, August 31st - September 4th in Boston, MA USA!
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...