Graph data structuresShared memory systemsThis paper investigates the performance of graph-structured analytics on large-scale shared memory systems. Graph analytics are highly demanding for efficient graph tra
A data flow computation is represented by a data flow graph (DFG) whose nodes represent functions, and arcs represent data dependencies between functions. In the graph, the values are represented as tokens on the arcs. Data flow processors are stored-program computers in which the stored program...
However, the same restrictions that enable the performance gains also make it difficult to express many of the important stages in a typical graph-analytics pipeline: constructing the graph, modifying its structure, or expressing computation that spans multiple graphs. As a consequence, existing graph...
As for CHP-212 and TR14, a genome graph was built using gGnome61 (v.0.1) and manually curated. To check amplicon structure correctness for the patient samples, in silico-simulated Nanopore reads were sampled from the reconstructed amplicon using an adapted version of PBSIM2 (ref. 72) (...
Performance is up to 20 times faster than a sequential version of scan running on a fast CPU, as shown in the graph in Figure 39-7. Also, thanks to the advantages provided by CUDA, we outperform an optimized OpenGL implementation running on the same GPU by up to a factor of seven...
How TigerGraph achieves fast data ingest, fast graph traversal, and deep link analytics even for large data sets
MicroRNAs (miRNAs) are produced from highly structured primary transcripts (pri-miRNAs) and regulate numerous biological processes in eukaryotes. Due to the extreme heterogeneity of these structures, the initial processing sites of plant pri-miRNAs and t
Partitioning large graphs is a recognised approach to addressing scalability issues in graph data management. However, if these partitionings are of a low quality then the performance of path queries (indeed, general pattern matching queries), greatly decreases [18]. Intuitively, any measure of this...
Intel® Parallel Studio XE 2020 Update 4 Release Notes 8 o Graph functionality has been added as a preview feature with limited functionality. • Intel® MPI Benchmarks: o Bug fixes. • Intel® MPI Library: o Implemented dynamic processes support in OFI/mlx provider (disabled by ...
Recently, non-volatile memory (NVM) technology has revolutionized the landscape of memory systems. With many advantages, such as non volatility and near ze