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This is the second half of my HPTS 2026 notes. Day 1 was mostly about AI and the importance of specifications . Day 2 and day 3 cover hardware, object storage, failures, consensus, and lakehouses. Session 5: Hardware will save us all (chair: Natassa Ailamaki) The Fastest Way to be the Fastest (Bobbi Yogatama, NVIDIA) GPUs and databases have a love-hate relationship, and Bobbi listed the three reasons: limited GPU memory capacity, the interconnect bottleneck, and engineering complexity. The first two are being fixed by the hardware roadmap. HBM is getting larger and faster. Interconnects and network fabrics are getting faster. But engineering complexity requires some work. No one is eager to rewrite their database to get it work with a GPU. Bobbi introduced Sirius , a GPU-native composable analytics engine. It offers a drop-in accelerator for your existing database, with GPU-native execution underneath. MICE is the design principle: modular, interoperable, composable, extensible. Bobbi ...

Jim Gray started HPTS  42 years ago, and it still runs the same way at Asilomar with 0 proceedings, 1 track, 100 invited people, and ample discussion breaks. It's one of my favorite events, and I look forward to attending it every two years. This is my...umm...fifth HPTS. I have been attending all of them since 2015 , but I seem to have missed it in 2019 . Yep, my life is a blur, even for me. Here are my notes from day 1. I will put day 2 and day 3 in a second post. Keynote: Reality is the Final Verifier. Two Key Gaps in Agentic Software Engineering (Ion Stoica, UC Berkeley) Ion started with the promising parts. LLMs are able to solve difficult math problems, and provide proofs for conjectures that have been outstanding for many decades (Ok, maybe this is not as promising to mathematicians). LLMs also play scientist with AlphaEvolve, and OpenEvolve. In summer 2025 Berkeley ran a seminar where students applied agentic tools to research problems. They took a published state-of-the-a...

Atlas Infinite , which dropped in public preview on September 29 alongside MongoDB 9.0, delivers a significant architectural update payload: compute-storage disaggregation. Separating compute from storage is not new , and there are well-known ways to do it. But what makes Atlas Infinite interesting is how MongoDB departs from the standard design to keep WiredTiger's efficiency and to ensure that the shared storage layer never sees customer data unencrypted. I am giving a high level overview of the architecture here distilling from the MongoDB Engineering blog post by Abhishek Chauhan, who led the effort. For detailed coverage, I refer you to that post. It is a great read, and parts 2 and 3 will drop soon. Why not earlier, and why now Disaggregated databases have existed for some time, so why did MongoDB wait? This is mainly because MongoDB didn't need disaggregation to scale out: when a workload outgrew one machine, sharding spread it across many nodes in a seamless manner.  M...

General methods that scale with computation will inevitably displace hand-engineered domain knowledge! Sutton’s Bitter Lesson hangs like a Sword of Damocles over all of us. This paper, which just dropped, applies that lesson to data agents, and says that as LLMs improve, they will quickly absorb the agent scaffolding researchers have spent the last few years painstakingly building. It argues that researchers should instead work on building curated contextual information about the data environment (aka. persistent semantic context) to help data agents be efficient across many queries. The Bitter Lesson for Data Agents The evaluation section aims to capture the Bitter Lesson in action. The authors compared general coding agents (the Codex harness with no task-specific engineering) against state-of-the-art human-designed data agents on two benchmarks, TAG-Bench and DAB. They use the same models on both sides, so the only variable is the scaffolding. They find that: Although the human-d...

My quest for a principled solution for metastable failures has taken me back to my roots on self-stabilization, as this recent paper related the problem to composition of self-stabilizing systems.  But, my literature search for recent work on composing self-stabilizing systems didn't yield anything useful. The layered stabilization idea was already in place by the early 2000s, and nothing fundamental seems to have been added since. Frustrating. So I decided to attack the problem using the concrete example I have. I had composed a rely-guarantee TLA+ model of a retry storm as two components with contracts . That model reproduces metastable failure because the composition that worked from good states failed to work when a large shock removes the base case that let the two conditions hold each other up. Searching for  rely-guarantee based composition from every state, turned up a 2017 control theory paper by Kim, Arcak and Seshia, "A Small Gain Theorem for Parametric Assume-Gua...

AI doomerism is everywhere these days. Every field, and recently humanity as a whole, has had its "we're finished" post. In contrast to the run-of-the-mill hot take, Jason Potts has written an economics paper on why academia is doomed . So let's dive in. What does a university sell? Potts says a university is really a platform. It is a hub that connects many different groups: undergrads, grads, teaching staff, research staff, employers, government, alumni, donors, parents, etc. Each group needs the others. Undergrad tuition helps pay for the research infrastructure for the professors whose research reputation drew the students there in the first place. International student fees provide funding for local students. Unfortunately, this isn't a diversified portfolio kind of situation, but more of a weakest-link setup. If you pull on one thread, several others start to come loose as well. ( Side remark : I will admit that I have always struggled to find the true custo...

Aleksey and I are back to reading papers live. This paper, Jetpack(OSDI '26), attempts building a universal 1-RTT fast-path framework that bolts onto existing leader-based consensus protocols with minimal modification. Why would we want this? Classic consensus protocols like Raft, Paxos, or Zab require two round-trip times (2 RTT) to commit a command: one RTT from client to leader, and another to replicate across followers. The extra RTT matters a lot for WAN deployments, so fast-path protocols (such as Fast Paxos, EPaxos, or SwiftPaxos) reduce this to 1 RTT by bypassing leader serialization, but unfortunately they tightly couple the fast path to the core protocol design. Production systems cannot easily swap out their battle-tested bespoke consensus engines, but if there was an add on that helped with latency especially in WAN deployments, that would be useful. The good news is that Jetpack is truly an add-on portable deal. It provides a shim layer that runs two execution paths i...