Supercomputers and AI compute are ill-suited for sparse and data-intensive computations because they are optimized for maximum dense matrix "FLOPS". We have designed the UpDown System – optimized for graph computing, streaming data ingestion and transformation as well as high-level programming. The result outperforms conventional CPU/GPU-based systems by 10-100x on an ISO-power basis.

UpDown's radical micro-architecture unleashes fine-grained parallelism: 1-cycle thread creation and management, 1-cycle messages. This enables efficient computation on 10-instruction thread invocations. Further, software-controlled split-transaction DRAM access unlocks the power of HBM's massive memory bandwidth. For irregular applications, UpDown datapath efficiency is 10x greater. UpDown performance on skewed-graph computations exceeds multicore CPU's (>100x) and GPU's (20-60x) in single-node configuration. Updown performance scales to 1,000 and 10,000-fold speedup on BFS, Pagerank, Triangle Count, K-truss and more.

UpDown data ingestion exceeds 5 billion/records/s/node (1000x CPU-based databases), reaching 100 trillion records/s. It enables a new class of streaming analytics and complex workflows. UpDown enables high level programming with a global address space, and a flexible map-reduce framework (KVMSR) coupled with an event-driven language (UDWeave). This enables easy vertex, edge-centric programming, and fits well for other data-parallel models such as relational/graphDB, sparse matrixes, and more.

UpDown was created under funding from IARPA's AGILE program, and is being commercialized by Chicago UpDown Computing, Inc. (www.chupdown.com).

Computing research is becoming increasingly expensive, complex, and concentrated. Emerging areas such as artificial intelligence and quantum computing often require specialized infrastructure, large datasets, substantial funding, technical staff, and extensive institutional capacity. Ambitious national initiatives such as the Genesis Mission further demonstrate the growing importance of coordinated research across academia, industry, national laboratories, and government. Yet the resources needed to participate in such efforts remain unevenly distributed.

Looking toward 2036, where will computing research take place, and who will be able to participate? Will the most consequential research become concentrated within a small number of well-resourced universities, companies, and national laboratories? What roles will smaller academic institutions, emerging companies, and individual researchers play?

This panel will bring together perspectives from academia, industry, and national laboratories to examine what counts as computing research, whether funding and infrastructure have become proxies for research excellence, and how institutional resources shape who can contribute. Panelists will discuss how cross-sector partnerships and national initiatives such as Genesis can broaden participation while preserving pathways for small teams, foundational and exploratory work, undergraduate-driven research, and institutions serving diverse students and regions.

Ultimately, the panel asks: How can we ensure that computing research in 2036 is shaped by the breadth and quality of its ideas—not only by where researchers work or the resources available to them?