This paper explores the trade-offs between on-loaded versus offloaded network stack processing for systems with varying CPU frequencies. This study explores the differences of onload and offload using experiments run at different DVFS settings to change the frequency, while measuring performance and power. This allows for a quantitative comparison of the the performance and power and trade-offs between onload and offload cards, with a wide range of CPU performances. The results show that there is often a significant performance increase in using offloaded cards especially at lower CPU frequencies, with only a small increase in power usage. This study also uses MPI profiling to analyze why some applications see a larger benefit than others. This paper's contributions are an analytical, quantitative analysis of the trade-offs between onload and offload. While there has been debate to this question, this is the first, to the authors' knowledge, analytical evaluation of the performance difference. The range of frequencies analyzed give insight on how this MPI might perform on different architectures, such as the low frequency, many-core CPUs. Finally, the power measurements allow for the study to provide further depth in the analysis.

Traditional point-to-point communication sends data only after the entirety of the data is available. This includes situations where multiple actors (e.g., threads) contribute to the send buffer. As a result, cases where the completion times of these actors are widely distributed may be lost opportunities for optimization because data ready to be sent is waiting to be transmitted. Fine-grained communication exposes these opportunities by allowing buffers to be divided into element s that can then be sent independently (see e.g., Partitioned Communication in Message Passing Interface v4.0). While some research has been directed at exploring the utility of such ‘early-bird’ transmission, the overall search space for finding the best performing actor completion timings and element counts is large. In this work, we present an abstract model of fine-grained communication based on the LogGP model and a complementary benchmark. We use the model to explore actor completion timing scenarios and identify trends in communication behavior based on factors such as overall message size and delay between actor completions. We evaluate the benchmarks on three systems utilizing distinct network technologies and show that: (i) smaller numbers of element s are able to exploit most of the benefit of early-bird communication, (ii) performance benefit will depend non-trivially on application behavior, and (iii) benefits are highly network-dependent.