Benchmarking CRBLASTER on the 350-MHz 49-core Maestro Development Board

TL;DR

This paper evaluates the performance of the CRBLASTER parallel processing framework on a 49-core Maestro Development Board, highlighting hardware-software interactions and the importance of real scientific applications in hardware testing.

Contribution

It provides the first benchmarking of CRBLASTER on the 49-core Maestro processor, demonstrating porting challenges, performance insights, and the value of real-world scientific applications for hardware evaluation.

Findings

Performance varies with hardware assist for floating-point operations.

Memory-intensive and computational-intensive applications reveal hardware subtleties.

Real scientific applications are crucial for effective hardware testing.

Abstract

I describe the performance of the CRBLASTER computational framework on a 350-MHz 49-core Maestro Development Board (MDB). The 49-core Interim Test Chip (ITC) was developed by the U.S. Government and is based on the intellectual property of the 64-core TILE64 processor of the Tilera Corporation. The Maestro processor is intended for use in the high radiation environments found in space; the ITC was fabricated using IBM 90-nm CMOS 9SF technology and Radiation-Hardening-by-Design (RHDB) rules. CRBLASTER is a parallel-processing cosmic-ray rejection application based on a simple computational framework that uses the high-performance computing industry standard Message Passing Interface (MPI) library. CRBLASTER was designed to be used by research scientists to easily port image-analysis programs based on embarrassingly-parallel algorithms to a parallel-processing environment such as a multi-node Beowulf cluster or multi-core processors using MPI. I describe my experience of porting CRBLASTER to the 64-core TILE64 processor, the Maestro simulator, and finally the 49-core Maestro processor itself. Performance comparisons using the ITC are presented between emulating all floating-point operations in software and doing all floating point operations with hardware assist from an IEEE-754 compliant Aurora FPU (floating point unit) that is attached to each of the 49 cores. Benchmarking of the CRBLASTER computational framework using the memory-intensive L.A.COSMIC cosmic ray rejection algorithm and a computational-intensive Poisson noise generator reveal subtleties of the Maestro hardware design. Lastly, I describe the importance of using real scientific applications during the testing phase of next-generation computer hardware; complex real-world scientific applications can stress hardware in novel ways that may not necessarily be revealed while executing simple applications or unit tests.