RNAi machinery cooperates with SWI/SNF complexes in nucleosome positioning at Transcriptional Start Sites

In Eukaryotes, Argonaute (AGO) proteins have a well-established role in the cytoplasm in post-transcriptional regulation of gene expression in association with different classes of small non-coding RNAs (sRNAs). In plants and yeast, it has been demonstrated that AGO proteins exert a role in the epigenetic regulation of chromatin modifications. Furthermore, AGO2 protein acts also in the nuclei of human cell lines and emerging literature reports that upon the transfection of sRNAs complementary to non-coding promoter transcripts, AGO2 is recruited on target promoters. Previous results in our laboratory demonstrated that AGO2 and SWI/SNF have a physical interaction, which is independent of RNA or DNA, in human cell lines. As SWI/SNF is the major chromatin-remodelling complex in human, these data suggest that AGO2 might participate in the regulation of chromatin plasticity. In eukaryotes, the proper organization of chromatin is essential for the control of gene expression and is achieved through the concerted activity of histone modifications, DNA methylation and nucleosome positioning. The focus of the present thesis has been the development of relevant bioinformatics pipelines for data processing, analysis and visualization, all aiming at dissection of the functional significance of the AGO2-SWI/SNF interaction. Interestingly, this bioinformatics pipeline allowed me to identify a novel class of nuclear AGO2-bound sRNAs arising from genomic regions 150 nt around the Transcription Start Sites (TSS) bound by SWI/SNF (swiRNAs). Furthermore, swiRNAs present a Dicer-dependent processing and show an involvement in nucleosome occupancy at nucleosome +1. These data represent the first description of a molecular mechanism through which AGO2 is involved in nucleosome positioning in mammalian cells

Preparing HPC Applications for the Exascale Era: A Decoupling Strategy

Production-quality parallel applications are often a mixture of diverse operations, such as computation- and communication-intensive, regular and irregular, tightly coupled and loosely linked operations. In conventional construction of parallel applications, each process performs all the operations, which might result inefficient and seriously limit scalability, especially at large scale. We propose a decoupling strategy to improve the scalability of applications running on large-scale systems. Our strategy separates application operations onto groups of processes and enables a dataflow processing paradigm among the groups. This mechanism is effective in reducing the impact of load imbalance and increases the parallel efficiency by pipelining multiple operations. We provide a proof-of-concept implementation using MPI, the de-facto programming system on current supercomputers. We demonstrate the effectiveness of this strategy by decoupling the reduce, particle communication, halo exchange and I/O operations in a set of scientific and data-analytics applications. A performance evaluation on 8,192 processes of a Cray XC40 supercomputer shows that the proposed approach can achieve up to 4x performance improvement.Comment: The 46th International Conference on Parallel Processing (ICPP-2017

Do Factors Associated with Increases in Higher Education Costs Affect Average Net Price at Four-Year Public and Private Not-for-Profit Institutions?

College costs have risen, and students and their families are questioning whether or not students can afford to attend. Factors such as instructional inefficiencies, administrative expenses, and federal financial aid have been associated with the rise in higher education costs at four-year public and four-year private not-for-profit institutions. In addition, reductions in state appropriations have been associated with an increase of tuition at four-year public institutions. But do these factors have a relationship with average net price? The findings of this study note that there is a relationship between average net price and these factors

Do Factors Associated with Increases in Higher Education Costs Affect Average Net Price at Four-Year Public and Private Not-for-Profit Institutions?

College costs have risen, and students and their families are questioning whether or not students can afford to attend. Factors such as instructional inefficiencies, administrative expenses, and federal financial aid have been associated with the rise in higher education costs at four-year public and four-year private not-for-profit institutions. In addition, reductions in state appropriations have been associated with an increase of tuition at four-year public institutions. But do these factors have a relationship with average net price? The findings of this study note that there is a relationship between average net price and these factors

Exploring the Performance Benefit of Hybrid Memory System on HPC Environments

Hardware accelerators have become a de-facto standard to achieve high performance on current supercomputers and there are indications that this trend will increase in the future. Modern accelerators feature high-bandwidth memory next to the computing cores. For example, the Intel Knights Landing (KNL) processor is equipped with 16 GB of high-bandwidth memory (HBM) that works together with conventional DRAM memory. Theoretically, HBM can provide 5x higher bandwidth than conventional DRAM. However, many factors impact the effective performance achieved by applications, including the application memory access pattern, the problem size, the threading level and the actual memory configuration. In this paper, we analyze the Intel KNL system and quantify the impact of the most important factors on the application performance by using a set of applications that are representative of scientific and data-analytics workloads. Our results show that applications with regular memory access benefit from MCDRAM, achieving up to 3x performance when compared to the performance obtained using only DRAM. On the contrary, applications with random memory access pattern are latency-bound and may suffer from performance degradation when using only MCDRAM. For those applications, the use of additional hardware threads may help hide latency and achieve higher aggregated bandwidth when using HBM

Idle Period Propagation in Message-Passing Applications

Idle periods on different processes of Message Passing applications are unavoidable. While the origin of idle periods on a single process is well understood as the effect of system and architectural random delays, yet it is unclear how these idle periods propagate from one process to another. It is important to understand idle period propagation in Message Passing applications as it allows application developers to design communication patterns avoiding idle period propagation and the consequent performance degradation in their applications. To understand idle period propagation, we introduce a methodology to trace idle periods when a process is waiting for data from a remote delayed process in MPI applications. We apply this technique in an MPI application that solves the heat equation to study idle period propagation on three different systems. We confirm that idle periods move between processes in the form of waves and that there are different stages in idle period propagation. Our methodology enables us to identify a self-synchronization phenomenon that occurs on two systems where some processes run slower than the other processes.Comment: 18th International Conference on High Performance Computing and Communications, IEEE, 201

Exploring Application Performance on Emerging Hybrid-Memory Supercomputers

Next-generation supercomputers will feature more hierarchical and heterogeneous memory systems with different memory technologies working side-by-side. A critical question is whether at large scale existing HPC applications and emerging data-analytics workloads will have performance improvement or degradation on these systems. We propose a systematic and fair methodology to identify the trend of application performance on emerging hybrid-memory systems. We model the memory system of next-generation supercomputers as a combination of "fast" and "slow" memories. We then analyze performance and dynamic execution characteristics of a variety of workloads, from traditional scientific applications to emerging data analytics to compare traditional and hybrid-memory systems. Our results show that data analytics applications can clearly benefit from the new system design, especially at large scale. Moreover, hybrid-memory systems do not penalize traditional scientific applications, which may also show performance improvement.Comment: 18th International Conference on High Performance Computing and Communications, IEEE, 201

Extending Message Passing Interface Windows to Storage

This work presents an extension to MPI supporting the one-sided communication model and window allocations in storage. Our design transparently integrates with the current MPI implementations, enabling applications to target MPI windows in storage, memory or both simultaneously, without major modifications. Initial performance results demonstrate that the presented MPI window extension could potentially be helpful for a wide-range of use-cases and with low-overhead