DECOMPOSER: Functional Decomposition of Monolithic Applications to Heterogeneous Resources in Disaggregated Environments

This paper analyzes the performance of disaggregating monolithic applications across heterogeneous hardware. A monolithic program with convolution functions was tested on AMD, Intel servers, and a BlueField-2 DPU (ARM). After disaggregation into independent processes, it was run on the same server and over a local network. Results showed that disaggregation maintained similar performance to the monolithic version, with minimal impact despite network latency. We also observed improved cache utilization, particularly a reduction in L1 cache misses and L2 accesses. Disaggregation shows promise in optimizing memory usage. Future work will focus on refining memory analysis and using GPUs and FPGAs to improve computational efficiency.