The Cell Broad Engine (BE) Processor has unique memory access architecture besides its powerful computing engines. Many computing-intensive applications have been ported to Cell BE successfully. But memory-intensive applications are rarely investigated except for several micro benchmarks. Since Cell BE has powerful software visible DMA engine, this paper studies on whether Cell BE is suit for applica- tions with large amount of random memory accesses. Two benchmarks, GUPS and SSCA#2, are used. The latter is a rather complex one that in representative of real world graph analysis applications. We find both benchmarks have good performance on Cell BE based IBM QS20 22. Com- pared with 2 conventional multi-processor systems with the same core thread number, GUPS is about 40-80 fast and SSCA#2 about 17-30 fast. The dynamic load balanc- ing and software pipeline for optimizing SSCA#2 are intro- duced. Based on the experiment, the potential of Cell BE for random access is analyzed in detail as well as its limita- tions of memory controller, atomic engine and TLB manage- ment.Our research shows although more programming effort are needed, Cell BE has the potencial for irregular memory access applications.
@cite_15 gave a detailed analysis of the communication performance of Cell BE using micro-benchmark, which encouraged our work on GUPS. They focused on the bus performance and did not give the result when large data set was used.
@cite_15
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Cellular radios consume more power and suffer reduced data rate when the signal is weak. According to our measurements, the communication energy per bit can be as much as 6x higher when the signal is weak than when it is strong. To realize energy savings, applications must preferentially communicate when the signal is strong, either by deferring non-urgent communication or by advancing anticipated communication to coincide with periods of strong signal. Allowing applications to perform such scheduling requires predicting signal strength, so that opportunities for energy-efficient communication can be anticipated. Furthermore, such prediction must be performed at little energy cost. In this paper, we make several contributions towards a practical system for energy-aware cellular data scheduling called Bartendr. First, we establish, via measurements, the relationship between signal strength and power consumption. Second, we show that location alone is not sufficient to predict signal strength and motivate the use of tracks to enable effective prediction. Finally, we develop energy-aware scheduling algorithms for different workloads - syncing and streaming - and evaluate these via simulation driven by traces obtained during actual drives, demonstrating energy savings of up to 60 . Our experiments have been performed on four cellular networks across two large metropolitan areas, one in India and the other in the U.S.