Sex-specific organisation of middle repetitive DNA sequences in the mealybug Planococcus lilacinus

Differential organisation of homologous chromosomes is related to both sex determination and genomic imprinting in coccid insects, the mealybugs. We report here the identification of two middle repetitive sequences that are differentially organised between the two sexes and also within the same diploid nucleus. These two sequences form a part of the male-specific nuclease-resistant chromatin (NRC) fraction of a mealybug Planococcus lilacinus. To understand the phenomenon of differential organisation we have analysed the components of NRC by cloning the DNA sequences present, deciphering their primary sequence, nucleosomal organisation, genomic distribution and cytological localisation, Our observations suggest that the middle repetitive sequences within NRC are functionally significant and we discuss their probable involvement in male-specific chromatin organisation.

Believe it or Not! Multicore CPUs can Match GPUs for FLOP-intensive Applications

In this work, we evaluate performance of a real-world image processing application that uses a cross-correlation algorithm to compare a given image with a reference one. The algorithm processes individual images represented as 2-dimensional matrices of single-precision floating-point values using O(n4) operations involving dot-products and additions. We implement this algorithm on a nVidia GTX 285 GPU using CUDA, and also parallelize it for the Intel Xeon (Nehalem) and IBM Power7 processors, using both manual and automatic techniques. Pthreads and OpenMP with SSE and VSX vector intrinsics are used for the manually parallelized version, while a state-of-the-art optimization framework based on the polyhedral model is used for automatic compiler parallelization and optimization. The performance of this algorithm on the nVidia GPU suffers from: (1) a smaller shared memory, (2) unaligned device memory access patterns, (3) expensive atomic operations, and (4) weaker single-thread performance. On commodity multi-core processors, the application dataset is small enough to fit in caches, and when parallelized using a combination of task and short-vector data parallelism (via SSE/VSX) or through fully automatic optimization from the compiler, the application matches or beats the performance of the GPU version. The primary reasons for better multi-core performance include larger and faster caches, higher clock frequency, higher on-chip memory bandwidth, and better compiler optimization and support for parallelization. The best performing versions on the Power7, Nehalem, and GTX 285 run in 1.02s, 1.82s, and 1.75s, respectively. These results conclusively demonstrate that, under certain conditions, it is possible for a FLOP-intensive structured application running on a multi-core processor to match or even beat the performance of an equivalent GPU version.

Building digital capability: Leading and managing an effective digital organisation (team, service, department)

This is a version of the Jisc ‘Six Elements of Digital Capabilities’ model, specifically to support Jisc’s members in the area of Digital Leadership. It is one example of the base model being used in a particular context and is not meant to be definitive.