Forest policies and programs affecting vulnerability and adaptation to climate change

Due to large scale afforestation programs and forest conservation legislations, India's total forest area seems to have stabilized or even increased. In spite of such efforts, forest fragmentation and degradation continues, with forests being subject to increased pressure due to anthropogenic factors. Such fragmentation and degradation is leading to the forest cover to change from very dense to moderately dense and open forest and 253 km(2) of very dense forest has been converted to moderately dense forest, open forest, scrub and non-forest (during 2005-2007). Similarly, there has been a degradation of 4,120 km(2) of moderately dense forest to open forest, scrub and non-forest resulting in a net loss of 936 km(2) of moderately dense forest. Additionally, 4,335 km(2) of open forest have degraded to scrub and non-forest. Coupled with pressure due to anthropogenic factors, climate change is likely to be an added stress on forests. Forest sector programs and policies are major factors that determine the status of forests and potentially resilience to projected impacts of climate change. An attempt is made to review the forest policies and programs and their implications for the status of forests and for vulnerability of forests to projected climate change. The study concludes that forest conservation and development policies and programs need to be oriented to incorporate climate change impacts, vulnerability and adaptation.

Intracellular Pathogen Sensor NOD2 Programs Macrophages to Trigger Notch1 Activation

Intracellular pathogen sensor, NOD2, has been implicated in regulation of wide range of anti-inflammatory responses critical during development of a diverse array of inflammatory diseases; however, underlying molecular details are still imprecisely understood. In this study, we demonstrate that NOD2 programs macrophages to trigger Notch1 signaling. Signaling perturbations or genetic approaches suggest signaling integration through cross-talk between Notch1-PI3K during the NOD2-triggered expression of a multitude of immunological parameters including COX-2/PGE(2) and IL-10. NOD2 stimulation enhanced active recruitment of CSL/RBP-Jk on the COX-2 promoter in vivo. Intriguingly, nitric oxide assumes critical importance in NOD2-mediated activation of Notch1 signaling as iNOS(-/-) macrophages exhibited compromised ability to execute NOD2-triggered Notch1 signaling responses. Correlative evidence demonstrates that this mechanism operates in vivo in brain and splenocytes derived from wild type, but not from iNOS(-/-) mice. Importantly, NOD2-driven activation of the Notch1-PI3K signaling axis contributes to its capacity to impart survival of macrophages against TNF-alpha or IFN-gamma-mediated apoptosis and resolution of inflammation. Current investigation identifies Notch1-PI3K as signaling cohorts involved in the NOD2-triggered expression of a battery of genes associated with anti-inflammatory functions. These findings serve as a paradigm to understand the pathogenesis of NOD2-associated inflammatory diseases and clearly pave a way toward development of novel therapeutics.

A Flat Concurrent Prolog compiler for PARAM

We describe a compiler for the Flat Concurrent Prolog language on a message passing multiprocessor architecture. This compiler permits symbolic and declarative programming in the syntax of Guarded Horn Rules, The implementation has been verified and tested on the 64-node PARAM parallel computer developed by C-DAC (Centre for the Development of Advanced Computing, India), Flat Concurrent Prolog (FCP) is a logic programming language designed for concurrent programming and parallel execution, It is a process oriented language, which embodies dataflow synchronization and guarded-command as its basic control mechanisms. An identical algorithm is executed on every processor in the network, We assume regular network topologies like mesh, ring, etc, Each node has a local memory, The algorithm comprises of two important parts: reduction and communication, The most difficult task is to integrate the solutions of problems that arise in the implementation in a coherent and efficient manner. We have tested the efficacy of the compiler on various benchmark problems of the ICOT project that have been reported in the recent book by Evan Tick, These problems include Quicksort, 8-queens, and Prime Number Generation, The results of the preliminary tests are favourable, We are currently examining issues like indexing and load balancing to further optimize our compiler.

The requirements for Ca2+, protein phosphorylation and concurrent protein synthesis for zeatin signaling of acidic chitinase transcript accumulation in Cucumis sativus L

We have previously reported that both Ca2+ and staurosporine-sensitive protein kinase(s) are involved in the cytokinin zeatin induction of cucumber chitinase activity and its protein content (Barwe et al. 2001). To further characterize signal transduction events involved in this cytokinin induction of chitinase gene expression, Northern hybridizations of total RNAs prepared from excised, dark-grown cucumber cotyledons treated with cytokinins and/or various agonists and antagonists of signal transduction components, were carried out using a cucumber acidic chitinase (CACHT) cDNA probe (Metraux et al. 1989). CACHT mRNA increased by approximately 5- to 6-fold in response to exogenous zeatin (Z), zeatin riboside (ZR), and benzyladenine (BA) treatment, but failed to accumulate in response to kinetin (K). Among the cytokinins tested, Z was most effective. The Z-induced accumulation of CACHT mRNA was inhibited by a plasma membrane Ca2+ channel blocker verapamil. Treatment of cotyledons with exogenous CaCl2 and calcium ionophore A23187 in the presence and absence of cytokinin enhanced CACHT mRNA accumulation. These two observations suggest the participation of extracellular calcium in signaling Z-induction. Furthermore, the presence of staurosporine (an inhibitor of protein kinase) in Z treatment reduced CACHT mRNA, suggesting the involvement of phosphorylation of one or more cellular proteins. In addition, we provide evidence that the Z-induction of CACHT mRNA is blocked by protein synthesis inhibitor cycloheximide treatment. Taken together, these results suggest that Ca2+ influx from extracellular space, protein phosphorylation, and concurrent protein synthesis events participate in cytokinin signaling during Z-induced CACHT transcript accumulation.

Automatic Compilation of MATLAB Programs for Synergistic Execution on Heterogeneous Processors

MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typical MATLAB programs have abundant data parallelism. These programs also have control flow dominated scalar regions that have an impact on the program's execution time. Today's computer systems have tremendous computing power in the form of traditional CPU cores and throughput oriented accelerators such as graphics processing units(GPUs). Thus, an approach that maps the control flow dominated regions to the CPU and the data parallel regions to the GPU can significantly improve program performance. In this paper, we present the design and implementation of MEGHA, a compiler that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors. Our solution is fully automated and does not require programmer input for identifying data parallel regions. We propose a set of compiler optimizations tailored for MATLAB. Our compiler identifies data parallel regions of the program and composes them into kernels. The problem of combining statements into kernels is formulated as a constrained graph clustering problem. Heuristics are presented to map identified kernels to either the CPU or GPU so that kernel execution on the CPU and the GPU happens synergistically and the amount of data transfer needed is minimized. In order to ensure required data movement for dependencies across basic blocks, we propose a data flow analysis and edge splitting strategy. Thus our compiler automatically handles composition of kernels, mapping of kernels to CPU and GPU, scheduling and insertion of required data transfer. The proposed compiler was implemented and experimental evaluation using a set of MATLAB benchmarks shows that our approach achieves a geometric mean speedup of 19.8X for data parallel benchmarks over native execution of MATLAB.

Fuzzy Clustering Based Ad Recommendation for TV Programs

Advertisements(Ads) are the main revenue earner for Television (TV) broadcasters. As TV reaches a large audience, it acts as the best media for advertisements of products and services. With the emergence of digital TV, it is important for the broadcasters to provide an intelligent service according to the various dimensions like program features, ad features, viewers’ interest and sponsors’ preference. We present an automatic ad recommendation algorithm that selects a set of ads by considering these dimensions and semantically match them with programs. Features of the ad video are captured interms of annotations and they are grouped into number of predefined semantic categories by using a categorization technique. Fuzzy categorical data clustering technique is applied on categorized data for selecting better suited ads for a particular program. Since the same ad can be recommended for more than one program depending upon multiple parameters, fuzzy clustering acts as the best suited method for ad recommendation. The relative fuzzy score called “degree of membership” calculated for each ad indicates the membership of a particular ad to different program clusters. Subjective evaluation of the algorithm is done by 10 different people and rated with a high success score.

Pathogen-specific TLR2 Protein Activation Programs Macrophages to Induce Wnt-beta-Catenin Signaling

Innate immunity recognizes and resists various pathogens; however, the mechanisms regulating pathogen versus non-pathogen discrimination are still imprecisely understood. Here, we demonstrate that pathogen-specific activation of TLR2 upon infection with Mycobacterium bovis BCG, in comparison with other pathogenic microbes, including Salmonella typhimurium and Staphylococcus aureus, programs macrophages for robust up-regulation of signaling cohorts of Wnt-beta-catenin signaling. Signaling perturbations or genetic approaches suggest that infection-mediated stimulation of Wnt-beta-catenin is vital for activation of Notch1 signaling. Interestingly, inducible NOS (iNOS) activity is pivotal for TLR2-mediated activation of Wnt-beta-catenin signaling as iNOS(-/-) mice demonstrated compromised ability to trigger activation of Wnt-beta-catenin signaling as well as Notch1-mediated cellular responses. Intriguingly, TLR2-driven integration of iNOS/NO, Wnt-beta-catenin, and Notch1 signaling contributes to its capacity to regulate the battery of genes associated with T(Reg) cell lineage commitment. These findings reveal a role for differential stimulation of TLR2 in deciding the strength of Wnt-beta-catenin signaling, which together with signals from Notch1 contributes toward the modulation of a defined set of effector functions in macrophages and thus establishes a conceptual framework for the development of novel therapeutics.

Petri net based performance modeling for effective DVFS for multithreaded programs

Dynamic Voltage and Frequency Scaling (DVFS) is a very effective tool for designing trade-offs between energy and performance. In this paper, we use a formal Petri net based program performance model that directly captures both the application and system properties, to find energy efficient DVFS settings for CMP systems, that satisfy a given performance constraint, for SPMD multithreaded programs. Experimental evaluation shows that we achieve significant energy savings, while meeting the performance constraints.

Evaluation of dynamic voltage and frequency scaling for stream programs

Dynamic Voltage and Frequency Scaling (DVFS) offers a huge potential for designing trade-offs involving energy, power, temperature and performance of computing systems. In this paper, we evaluate three different DVFS schemes - our enhancement of a Petri net performance model based DVFS method for sequential programs to stream programs, a simple profile based Linear Scaling method, and an existing hardware based DVFS method for multithreaded applications - using multithreaded stream applications, in a full system Chip Multiprocessor (CMP) simulator. From our evaluation, we find that the software based methods achieve significant Energy/Throughput2(ET−2) improvements. The hardware based scheme degrades performance heavily and suffers ET−2 loss. Our results indicate that the simple profile based scheme achieves the benefits of the complex Petri net based scheme for stream programs, and present a strong case for the need for independent voltage/frequency control for different cores of CMPs, which is lacking in most of the state-of-the-art CMPs. This is in contrast to the conclusions of a recent evaluation of per-core DVFS schemes for multithreaded applications for CMPs.

Automatic compilation of MATLAB programs for synergistic execution on heterogeneous processors

MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typical MATLAB programs have abundant data parallelism. These programs also have control flow dominated scalar regions that have an impact on the program's execution time. Today's computer systems have tremendous computing power in the form of traditional CPU cores and throughput oriented accelerators such as graphics processing units(GPUs). Thus, an approach that maps the control flow dominated regions to the CPU and the data parallel regions to the GPU can significantly improve program performance. In this paper, we present the design and implementation of MEGHA, a compiler that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors. Our solution is fully automated and does not require programmer input for identifying data parallel regions. We propose a set of compiler optimizations tailored for MATLAB. Our compiler identifies data parallel regions of the program and composes them into kernels. The problem of combining statements into kernels is formulated as a constrained graph clustering problem. Heuristics are presented to map identified kernels to either the CPU or GPU so that kernel execution on the CPU and the GPU happens synergistically and the amount of data transfer needed is minimized. In order to ensure required data movement for dependencies across basic blocks, we propose a data flow analysis and edge splitting strategy. Thus our compiler automatically handles composition of kernels, mapping of kernels to CPU and GPU, scheduling and insertion of required data transfer. The proposed compiler was implemented and experimental evaluation using a set of MATLAB benchmarks shows that our approach achieves a geometric mean speedup of 19.8X for data parallel benchmarks over native execution of MATLAB.

Tiling stencil computations to maximize parallelism

Most stencil computations allow tile-wise concurrent start, i.e., there always exists a face of the iteration space and a set of tiling hyperplanes such that all tiles along that face can be started concurrently. This provides load balance and maximizes parallelism. However, existing automatic tiling frameworks often choose hyperplanes that lead to pipelined start-up and load imbalance. We address this issue with a new tiling technique that ensures concurrent start-up as well as perfect load-balance whenever possible. We first provide necessary and sufficient conditions on tiling hyperplanes to enable concurrent start for programs with affine data accesses. We then provide an approach to find such hyperplanes. Experimental evaluation on a 12-core Intel Westmere shows that our code is able to outperform a tuned domain-specific stencil code generator by 4% to 27%, and previous compiler techniques by a factor of 2x to 10.14x.

Queuing of concurrent movement plans by Basal Ganglia

How the brain converts parallel representations of movement goals into sequential movements is not known. We tested the role of basal ganglia (BG) in the temporal control of movement sequences by a convergent approach involving inactivation of the BG by muscimol injections into the caudate nucleus of monkeys and assessing behavior of Parkinson's disease patients, performing a modified double-step saccade task. We tested a critical prediction of a class of competitive queuing models that explains serial behavior as the outcome of a selection of concurrently activated goals. In congruence with these models, we found that inactivation or impairment of the BG unmasked the parallel nature of goal representations such that a significantly greater extent of averaged saccades, curved saccades, and saccade sequence errors were observed. These results suggest that the BG perform a form of competitive queuing, holding the second movement plan in abeyance while the first movement is being executed, allowing the proper temporal control of movement sequences.

Improving GPGPU Concurrency with Elastic Kernels

Each new generation of GPUs vastly increases the resources available to GPGPU programs. GPU programming models (like CUDA) were designed to scale to use these resources. However, we find that CUDA programs actually do not scale to utilize all available resources, with over 30% of resources going unused on average for programs of the Parboil2 suite that we used in our work. Current GPUs therefore allow concurrent execution of kernels to improve utilization. In this work, we study concurrent execution of GPU kernels using multiprogram workloads on current NVIDIA Fermi GPUs. On two-program workloads from the Parboil2 benchmark suite we find concurrent execution is often no better than serialized execution. We identify that the lack of control over resource allocation to kernels is a major serialization bottleneck. We propose transformations that convert CUDA kernels into elastic kernels which permit fine-grained control over their resource usage. We then propose several elastic-kernel aware concurrency policies that offer significantly better performance and concurrency compared to the current CUDA policy. We evaluate our proposals on real hardware using multiprogrammed workloads constructed from benchmarks in the Parboil 2 suite. On average, our proposals increase system throughput (STP) by 1.21x and improve the average normalized turnaround time (ANTT) by 3.73x for two-program workloads when compared to the current CUDA concurrency implementation.

TCP: thread contention predictor for parallel programs

With proliferation of chip multicores (CMPs) on desktops and embedded platforms, multi-threaded programs have become ubiquitous. Existence of multiple threads may cause resource contention, such as, in on-chip shared cache and interconnects, depending upon how they access resources. Hence, we propose a tool - Thread Contention Predictor (TCP) to help quantify the number of threads sharing data and their sharing pattern. We demonstrate its use to predict a more profitable shared, last level on-chip cache (LLC) access policy on CMPs. Our cache configuration predictor is 2.2 times faster compared to the cycle-accurate simulations. We also demonstrate its use for identifying hot data structures in a program which may cause performance degradation due to false data sharing. We fix layout of such data structures and show up-to 10% and 18% improvement in execution time and energy-delay product (EDP), respectively.