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The following is a very common question in numerous theoretical and application-related domains: given a graph G, does it satisfy some given property? For example, is G connected? Is its diameter smaller than a given threshold? Is its average degree larger than a certain threshold? Traditionally, algorithms to quickly answer such questions were developed for static and centralized graphs (i.e. G is...
A Suffix tree is a fundamental and versatile string data structure that is frequently used in important application areas such as text processing, information retrieval, and computational biology. Sequentially, the construction of suffix trees takes linear time, and optimal parallel algorithms exist only for the PRAM model. Recent works mostly target low core-count shared-memory implementations but...
Ordering vertices of a graph is key to minimize fill-in and data structure size in sparse direct solvers, maximize locality in iterative solvers, and improve performance in graph algorithms. Except for naturally parallelizable ordering methods such as nested dissection, many important ordering methods have not been efficiently mapped to distributed-memory architectures. In this paper, we present the...
Vectorization and GPUs will profoundly change graph processing. Traditional graph algorithms tuned for 32- or 64-bit based memory accesses will be inefficient on architectures with 512-bit wide (or larger) instruction units that are already present in the Intel Knights Landing (KNL) manycore CPU. Anticipating this shift, we propose SlimSell: a vectorizable graph representation to accelerate Breadth-First...
Computer architectures continue to develop rapidly towards massively parallel and heterogeneous systems. Thus, easily extensible yet highly efficient parallelization approaches for a variety of platforms are urgently needed. In this paper, we present SWhybrid, a hybrid computing framework for large-scale biological sequence database search on heterogeneous computing environments with multi-core or...
The progress of next-generation sequencing has a major impact on medical and genomic research. This technology can now produce billions of short DNA fragments (reads) in a single run. One of the most demanding computational problems used by almost every sequencing pipeline is short-read alignment; i.e. determining where each fragment originated from in the original genome. Most current solutions are...
Finding regions of local similarity between biological sequences is a fundamental task in computational biology. BLAST is the most widely-used tool for this purpose, but it suffers from irregularities due to its heuristic nature. To achieve fast search, recent approaches construct the index from the database instead of the input query. However, database indexing introduces more challenges in the design...
Data movement is increasingly becoming the bottleneck of both performance and energy efficiency in modern computation. Until recently, it was the case that there is limited freedom for communication optimization on GPUs, as conventional GPUs only provide two types of methods for inter-thread communication: using shared memory or global memory. However, a new warp shuffle instruction has been introduced...
GPUs provide high-bandwidth/low-latency on-chip shared memory and L1 cache to efficiently service a large number of concurrent memory requests (to contiguous memory space). To support warp-wide accesses to L1 cache, GPU L1 cache lines are very wide. However, such L1 cache architecture cannot always be efficiently utilized when applications generate many memory requests with irregular access patterns...
Spin-Transfer Torque Magnetoresistive Random-Access Memory (STT-MRAM) is a promising memory technology, which has high density, fast read speed, low leakage power, and non-volatility, and is suitable for multi-core on-chip last-level caches. However, the high write energy and latency, as well as less-than-desirable write endurance of STT-MRAM remain challenges. This paper proposes a new encoded content-aware...
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