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_8 presented a software thread idea for list-ranking, which induced us for the GUPS implementation. For the SSCA2, the irregular data size makes much trouble for thread partition. So eventually we used a software pipeline method instead.
@cite_8
2040329636
In this paper, we address two problems in Sparse Subspace Clustering algorithm (SSC), i.e., scalability issue and out-of-sample problem. SSC constructs a sparse similarity graph for spectral clustering by using l1-minimization based coefficients, has achieved state-of-the-art results for image clustering and motion segmentation. However, the time complexity of SSC is proportion to the cubic of problem size such that it is inefficient to apply SSC into large scale setting. Moreover, SSC does not handle with out-of-sample data that are not used to construct the similarity graph. For each new datum, SSC needs recalculating the cluster membership of the whole data set, which makes SSC is not competitive in fast online clustering. To address the problems, this paper proposes out-of-sample extension of SSC, named as Scalable Sparse Subspace Clustering (SSSC), which makes SSC feasible to cluster large scale data sets. The solution of SSSC adopts a "sampling, clustering, coding, and classifying" strategy. Extensive experimental results on several popular data sets demonstrate the effectiveness and efficiency of our method comparing with the state-of-the-art algorithms.