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. For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association. In this paper, we study spectrum management in a two-tier microcell macrocell cellular system. Two issues are studied: micro-macro cell selection and frequency spectrum partitioning between microcells and macrocells. To keep the handoff rate in a two-tier cellular system at an acceptable level, low mobility users (with speed spl upsi V sub 0 ) should undergo handoffs at macrocell boundaries. The mobile determines user mobility from microcell sojourn times and uses it for channel assignment at call origination and handoff. The probability of erroneous assignment of a mobile to a microcell or macrocell is shown to be significantly lower than previous approaches. We investigate the optimal velocity threshold, V sub 0 , and propose that it may be dynamically adjusted according to traffic load. Finally, we propose a systematic way for finding an optimal partition of frequency spectrum between microcells and macrocells. This partitioning is based on the traffic load and velocity distribution of mobiles in the system. This paper presents a first fully analytical approach to performance evaluation of D2D communication systems, which does not assume the system to be in saturated conditions. We adopt a Coupled Processors model to describe a cellular scenario with D2D users sharing radio resources with cellular users, i.e., adopting in-band underlay D2D schemes. We derive sufficient conditions for stability of such system, characterizing the effect of D2D transmissions on cellular user performance. Moreover, we present a computationally feasible method for the determination of a proportionally fair allocation of resources. We show that, in non-saturated networks, such an allocation sensibly improves the one derived under the assumption of saturation. Our results show the importance of accurately modelling the interdependence in users performance in the design and evaluation of a D2D cellular system.