Quantitative analysis of sequence alignment applications on multiprocessor architectures

Castaño, Friman Sánchez; Ramírez, Alex; Valero, Mateo

doi:10.1145/1531743.1531755

Cited by 5 publications

(2 citation statements)

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“…There are multiple levels of parallelism in the protein sequence alignment problem. The most frequently exploited is the embarrassingly parallel situation where a collection of query sequences has to be aligned to a database of candidate sequences [44]. The parallel strategy, to align one query sequence to a single candidate, is based on the FASTA Smith-Waterman code (ssearch) [35].…”

Section: Fasta Smith-watermanmentioning

confidence: 99%

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Castell: a heterogeneous cmp architecture scalable to hundreds of processors

Cabarcas Jaramillo

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Technology improvements and power constrains have taken multicore architectures to dominate microprocessor designs over uniprocessors. At the same time, accelerator based architectures have shown that heterogeneous multicores are very efficient and can provide high throughput for parallel applications, but with a high-programming effort. We propose Castell a scalable chip multiprocessor architecture that can be programmed as uniprocessors, and provides the high throughput of accelerator-based architectures. Castell relies on task-based programming models that simplify software development. These models use a runtime system that dynamically finds, schedules, and adds hardware-specific features to parallel tasks. One of these features is DMA transfers to overlap computation and data movement, which is known as double buffering. This feature allows applications on Castell to tolerate large memory latencies and lets us design the memory system focusing on memory bandwidth. In addition to provide programmability and the design of the memory system, we have used a hierarchical NoC and added a synchronization module. The NoC design distributes memory traffic efficiently to allow the architecture to scale. The synchronization module is a consequence of the large performance degradation of application for large synchronization latencies. Castell is mainly an architecture framework that enables the definition of domain-specific implementations, fine-tuned to a particular problem or application. So far, Castell has been successfully used to propose heterogeneous multicore architectures for scientific kernels, video decoding (using H.264), and protein sequence alignment (using Smith-Waterman and clustalW). It has also been used to explore a number of architecture optimizations such as enhanced DMA controllers, and architecture support for task-based programming models. iii

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Section: Fasta Smith-watermanmentioning

confidence: 99%

“…Similarly, the semaphore table can contain 4096 physical entries for Tags, Value, Head and Tail. This distribution would provide 32 bit Tag plus 12 bit Index semaphores, which gives 44 bit physical semaphores or 2 44 .…”

Section: Required Hardwarementioning

confidence: 99%

Castell: a heterogeneous cmp architecture scalable to hundreds of processors

Cabarcas Jaramillo

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Scalable multicore architectures for long DNA sequence comparison

Sánchez

Cabarcas

Ramírez

et al. 2011

Concurrency and Computation

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SUMMARYBiological sequence comparison is one of the most important tasks in Bioinformatics. Owing to the fast growth of databases that contain biological information, sequence comparison represents an important challenge for high-performance computing, especially when very long sequences are compared, i.e. the complete genome of several organisms. The Smith-Waterman (SW) algorithm is an exact method based on dynamic programming to quantify local similarity between sequences. The inherent large parallelism of the algorithm makes it ideal for architectures supporting multiple dimensions of parallelism (TLP, DLP and ILP). Concurrently, there is a paradigm shift towards chip multiprocessors in computer architecture, which offer a huge amount of potential performance that can only be exploited efficiently if applications are effectively mapped and parallelized. In this work, we analyze how large-scale biology sequence comparison takes advantage of the current and future multicore architectures. Our starting point is the performance analysis of the current multicore IBM Cell B.E. processor; we analyze two different SW implementations on the Cell B.E. Then, using simulation tools, we study the performance scalability when a many-core architecture is used for performing long DNA sequence comparison. We investigate the efficient memory organization that delivers the maximum bandwidth with the minimum cost. Our results show that a heterogeneous architecture can be an efficient alternative to execute challenging bioinformatic workloads.

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