Disease burden due to Streptococcus dysgalactiae subsp equisimilis (group G and C streptococcus) is higher than that due to Streptococcus pyogenes among Mumbai school children

Streptococcus pyogenes [group A streptococcus (GAS)], a human pathogen, and Streptococcus dysgalactiae subsp. equisimilis [human group G and C streptococcus (GGS/GCS)] are evolutionarily related, share the same tissue niche in humans, exchange genetic material, share up to half of their virulence-associated genes and cause a similar spectrum of diseases. Yet, GGS/GCS is often considered as a commensal bacterium and its role in streptococcal disease burden is under-recognized. While reports of the recovery of GGS/GCS from normally sterile sites are increasing, studies describing GGS/GCS throat colonization rates relative to GAS in the same population are very few. This study was carried out in India where the burden of streptococcal diseases, including rheumatic fever and rheumatic heart disease, is high. As part of a surveillance study, throat swabs were taken from 1504 children attending 7 municipal schools in Mumbai, India, during 2006-2008. GAS and GGS/GCS were identified on the basis of beta-haemolytic activity, carbohydrate group and PYR test, and were subsequently typed. The GGS/GCS carriage rate (1166/1504, 11%) was eightfold higher than the GAS carriage (22/1504, 1.5%) rate in this population. The 166 GGS/GCS isolates collected represented 21 different emm types (molecular types), and the 22 GAS isolates represented 15 different emm types. Although the rate of pharyngitis associated with GGS/GCS is marginally lower than with GAS, high rates of throat colonization by GGS/GCS underscore its importance in the pathogenesis of pharyngitis.

Towards building an intelligent decision support system for project management

Management of large projects, especially the ones in which a major component of R&D is involved and those requiring knowledge from diverse specialised and sophisticated fields, may be classified as semi-structured problems. In these problems, there is some knowledge about the nature of the work involved, but there are also uncertainties associated with emerging technologies. In order to draw up a plan and schedule of activities of such a large and complex project, the project manager is faced with a host of complex decisions that he has to take, such as, when to start an activity, for how long the activity is likely to continue, etc. An Intelligent Decision Support System (IDSS) which aids the manager in decision making and drawing up a feasible schedule of activities while taking into consideration the constraints of resources and time, will have a considerable impact on the efficient management of the project. This report discusses the design of an IDSS that helps in project planning phase through the scheduling phase. The IDSS uses a new project scheduling tool, the Project Influence Graph (PIG).

Fast and efficient automatic memory management for GPUs using compiler-assisted runtime coherence scheme

Exploiting the performance potential of GPUs requires managing the data transfers to and from them efficiently which is an error-prone and tedious task. In this paper, we develop a software coherence mechanism to fully automate all data transfers between the CPU and GPU without any assistance from the programmer. Our mechanism uses compiler analysis to identify potential stale accesses and uses a runtime to initiate transfers as necessary. This allows us to avoid redundant transfers that are exhibited by all other existing automatic memory management proposals. We integrate our automatic memory manager into the X10 compiler and runtime, and find that it not only results in smaller and simpler programs, but also eliminates redundant memory transfers. Tested on eight programs ported from the Rodinia benchmark suite it achieves (i) a 1.06x speedup over hand-tuned manual memory management, and (ii) a 1.29x speedup over another recently proposed compiler--runtime automatic memory management system. Compared to other existing runtime-only and compiler-only proposals, it also transfers 2.2x to 13.3x less data on average.

Automatic Data Allocation and Buffer Management for Multi-GPU Machines

Multi-GPU machines are being increasingly used in high-performance computing. Each GPU in such a machine has its own memory and does not share the address space either with the host CPU or other GPUs. Hence, applications utilizing multiple GPUs have to manually allocate and manage data on each GPU. Existing works that propose to automate data allocations for GPUs have limitations and inefficiencies in terms of allocation sizes, exploiting reuse, transfer costs, and scalability. We propose a scalable and fully automatic data allocation and buffer management scheme for affine loop nests on multi-GPU machines. We call it the Bounding-Box-based Memory Manager (BBMM). BBMM can perform at runtime, during standard set operations like union, intersection, and difference, finding subset and superset relations on hyperrectangular regions of array data (bounding boxes). It uses these operations along with some compiler assistance to identify, allocate, and manage data required by applications in terms of disjoint bounding boxes. This allows it to (1) allocate exactly or nearly as much data as is required by computations running on each GPU, (2) efficiently track buffer allocations and hence maximize data reuse across tiles and minimize data transfer overhead, and (3) and as a result, maximize utilization of the combined memory on multi-GPU machines. BBMM can work with any choice of parallelizing transformations, computation placement, and scheduling schemes, whether static or dynamic. Experiments run on a four-GPU machine with various scientific programs showed that BBMM reduces data allocations on each GPU by up to 75% compared to current allocation schemes, yields performance of at least 88% of manually written code, and allows excellent weak scaling.