In recent years, the sketch-based technique has been presented as an effective method for counting stream items on processors with limited storage and processing capabilities, such as the network processors. In this paper, we examine the implementation of a sketch-based counting algorithm on the heterogeneous multi-core Cell processor. Like the network processors, the Cell also contains on-chip special processors with limited local memories. These special processors enable parallel processing of stream items using short-vector data-parallel (SIMD) operations. We demonstrate that the inaccuracies of the estimates computed by straightforward adaptations of current sketch-based counting approaches are exacerbated by increased inaccuracies in approximating counts of low frequency items, and by the inherent space limitations of the Cell processor. To address these concerns, we implement a sketch-based counting algorithm, FCM, that is specifically adapted for the Cell processor architecture. FCM incorporates novel capabilities for improving estimation accuracy using limited space by dynamically identifying low- and high-frequency stream items, and using a variable number of hash functions per item as determined by an item's current frequency phase. We experimentshortally demonstrate that with similar space consumption, FCM computes better frequency estimates of both the low- and high-frequency items than a naive parallelization of an existing stream counting algorithm. Using FCM as the kernel, our parallel algorithm is able to scale the over all performance linearly as well as improve the estimate accuracy as the number of processors is increased. Thus, this work demonstrates the importance of adapting the algorithm to the specifics of the underlying architecture.

BACKGROUND: The chemical property and biological function of a protein is a direct consequence of its primary structure. Several algorithms have been developed which determine alignment and similarity of primary protein sequences. However, character based similarity cannot provide insight into the structural aspects of a protein. We present a method based on spectral similarity to compare subsequences of amino acids that behave similarly but are not aligned well by considering amino acids as mere characters. This approach finds a similarity score between sequences based on any given attribute, like hydrophobicity of amino acids, on the basis of spectral information after partial conversion to the frequency domain. RESULTS: Distance matrices of various branches of the human kinome, that is the full complement of human kinases, were developed that matched the phylogenetic tree of the human kinome establishing the efficacy of the global alignment of the algorithm. PKCd and PKCe kinases share close biological properties and structural similarities but do not give high scores with character based alignments. Detailed comparison established close similarities between subsequences that do not have any significant character identity. We compared their known 3D structures to establish that the algorithm is able to pick subsequences that are not considered similar by character based matching algorithms but share structural similarities. Similarly many subsequences with low character identity were picked between xyna-theau and xyna-clotm F/10 xylanases. Comparison of 3D structures of the subsequences confirmed the claim of similarity in structure. CONCLUSION: An algorithm is developed which is inspired by successful application of spectral similarity applied to music sequences. The method captures subsequences that do not align by traditional character based alignment tools but give rise to similar secondary and tertiary structures. The Spectral Similarity Score (SSS) is an extension to the conventional similarity methods and results indicate that it holds a strong potential for analysis of various biological sequences and structural variations in proteins.