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Highly Homologous Filamin Polypeptides Have Different Distributions in Avian Slow and Fast Muscle Fibers
The high molecular weight actin-binding protein filamin is located at the periphery of the Z disk in the fast adult chicken pectoral muscle (Gomer, R. H., and E. Lazarides, 1981, Cell, 23: 524-532). In contrast, we have found that in the slow anterior latissimus dorsi (ALD) muscle, filamin was additionally located throughout the l band as judged by immunofluorescence with affinity-purified antibodies on myofibrils and cryosections. The Z line proteins desmin and alpha-actinin, however, had the same distribution in ALD as they do in pectoral muscle. Quantitation of filamin and actin from the two muscle types showed that there was approximately 10 times as much filamin per actin in ALD myofibrils as in pectoral myofibrils. Filamin immunoprecipitated from ALD had an electrophoretic mobility in SDS polyacrylamide gels identical to that of pectoral myofibril filamin and slightly greater than that of chicken gizzard filamin. Two-dimensional peptide maps of filamin immunoprecipitated and labeled with ^(125)I showed that ALD myofibril filamin was virtually identical to pectoral myofibril filamin and was distinct from chicken gizzard filamin
Locality-aware parallel block-sparse matrix-matrix multiplication using the Chunks and Tasks programming model
We present a method for parallel block-sparse matrix-matrix multiplication on
distributed memory clusters. By using a quadtree matrix representation, data
locality is exploited without prior information about the matrix sparsity
pattern. A distributed quadtree matrix representation is straightforward to
implement due to our recent development of the Chunks and Tasks programming
model [Parallel Comput. 40, 328 (2014)]. The quadtree representation combined
with the Chunks and Tasks model leads to favorable weak and strong scaling of
the communication cost with the number of processes, as shown both
theoretically and in numerical experiments.
Matrices are represented by sparse quadtrees of chunk objects. The leaves in
the hierarchy are block-sparse submatrices. Sparsity is dynamically detected by
the matrix library and may occur at any level in the hierarchy and/or within
the submatrix leaves. In case graphics processing units (GPUs) are available,
both CPUs and GPUs are used for leaf-level multiplication work, thus making use
of the full computing capacity of each node.
The performance is evaluated for matrices with different sparsity structures,
including examples from electronic structure calculations. Compared to methods
that do not exploit data locality, our locality-aware approach reduces
communication significantly, achieving essentially constant communication per
node in weak scaling tests.Comment: 35 pages, 14 figure
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