HPTS 2026 (Day 2 and Day 3)

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 showed how Sirius plugs into DuckDB with no modification to DuckDB. The other design lesson from Bobby is to spend compute to save memory. On a GPU, compute is the abundant resource and memory capacity and bandwidth are the scarce ones, so the classic tradeoff needs to flip here. It is best to compress the data, compress everywhere, and operate on the compressed data. The Next Watt Saved Won't Come from a CPU or a GPU (Devashish Purandare, Microsoft) Datacenter power consumption is the bottleneck in building datacenters right now. Where is the power consumed? Tax cycles are 60-80% of all execution. There is datacenter tax: compression, crypto, data movement, memory allocation, protocol and RPC handling. Then there is also system tax: error handling, checksums, filesystems, kernel, STL, networking, multithreading. Unfortunately, no single category dominates, so there is no single high value thing to fix. So how do we fix this? Dev titled his next section: how I learned to stop worrying about Amdahl's law and love the accelerator. Again, unfortunately, both on-chip accelerators and off-chip accelerators have big drawbacks. On-chip you are stuck with whatever the CPU vendor gave you. Off-chip you pay to get data there and back. Dev's answer to this is yes. Cluster smaller accelerators together. That brings us to the fungible DPU, from the company Fungible, which Microsoft acquired and which is now the Azure Boost DPU. Six cores times four threads, and every task gets its own …