Gigadawgs

Mississippi State University

Diagram

Hardware

Hardware was selected based on budget, availability, and our familiarity with the platform. The design builds on a configuration the student organization has successfully used before, ensuring compatibility with the internal documentation and tooling we’ve already developed.

A key design decision was to prioritize improved networking capabilities over the built‑in networking found on standard Raspberry Pis. In multi‑node computing, the Pis themselves are rarely the primary bottleneck, network throughput and latency usually are. Strengthening the network layer therefore provides a more meaningful performance gain.

Additionally, storage is distributed across the cluster to enhance overall performance and resilience, with several Raspberry Pis serving as both compute and storage nodes.

Power monitoring

The current plan for power monitoring is to use a Kill‑A‑Watt meter connected at the point where the cluster and its networking equipment draw mains power, with its readings livestreamed for real‑time observation.

Hardware Table

Software

Strategy

Benchmarks

HPL: Perform an N and NB parameter sweep to identify the optimal problem size that maximizes TFLOPS.

D-LLAMA: Evaluate the trade-offs between 4-bit/8-bit quantization to achieve the highest possible token-per-second throughput for Llama 3 8B.

MDTest:Tune the parallel file system metadata servers and experiment with directory stripping to maximize IOPS for file creation and stat operations.

Applications

IQ-TREE: Implement a hybrid MPI+OpenMP execution model to minimize communication overhead while maximizing core utilization during complex phylogenetic tree reconstructions.

Mystery Application: Assign a 2-3 person team based on the individual strengths of the team members.

Team Details