Fuse-N: Framework for unified simulation environment for network-on-chip

Steady advancements in semiconductor technology over the past few decades have marked incipience of Multi-Processor System-on-Chip (MPSoCs). Owing to the inability of traditional bus-based communication system to scale well with improving microchip technologies, researchers have proposed Network-on-Chip (NoC) as the on-chip communication model. Current uni-processor centric modeling methodology does not address the new design challenges introduced by MPSoCs, thus calling for efficient simulation frameworks capable of capturing the interplay between the application, the architecture, and the network. Addressing these new challenges requires a framework that assists the designer at different abstraction levels of system design; This thesis concentrates on developing a framework for unified simulation environment for NoCs (fuse-N) which simplifies the design space exploration for NoCs by offering a comprehensive simulation support. The framework synthesizes the network infrastructure and the communication model and optimizes application mapping for design constraints. The proposed framework is a hardware-software co-design implementation using SystemC 2.1 and C++. Simulation results show the architectural, network and resource allocation behavior and highlight the quantitative relationships between various design choices; Also, a novel off-line non-preemptive static Traffic Aware Scheduling (TAS) policy is proposed for hard NoC platforms. The proposed scheduling policy maps the application onto the NoC architecture keeping track of the network traffic, which is generated with every resource and communication path allocation. TAS has been evaluated for various design metrics such as application completion time, resource utilization and task throughput. Simulation results show significant improvements over traditional approaches

Optimizing interactive analytics engines for heterogeneous clusters

This thesis targets the growing area of interactive data analytics engines. It builds upon a system called Getafix, an intelligent data replication and placement algorithm, and optimizes Getafix for running mixed queries over a heterogeneous cluster. The new algorithm is called Getafix-H, a cluster aware version of Getafix replication algorithm, with built-in optimizations for segment balancing and cluster auto-tiering. We integrated Getafix-H as an extension to Getafix inside Druid, a modern open-source interactive data analytics engine. We present experimental results using workloads from Yahoo!’s production Druid cluster. Compared to Getafix, Getafix-H improves the tail latency by 18% and reduces memory usage by up to 27% (2-3X improvement over Scarlett). In presence of stragglers, Getafix-H improves tail latency by 55% and reduces memory usage by upto 20% compared to Getafix. Getafix-H enables sysadmins to auto-tier a heterogeneous cluster with the tiering accuracy of up to 80%

PrismDB: Read-aware Log-structured Merge Trees for Heterogeneous Storage

In recent years, emerging hardware storage technologies have focused on divergent goals: better performance or lower cost-per-bit of storage. Correspondingly, data systems that employ these new technologies are optimized either to be fast (but expensive) or cheap (but slow). We take a different approach: by combining multiple tiers of fast and low-cost storage technologies within the same system, we can achieve a Pareto-efficient balance between performance and cost-per-bit. This paper presents the design and implementation of PrismDB, a novel log-structured merge tree based key-value store that exploits a full spectrum of heterogeneous storage technologies (from 3D XPoint to QLC NAND). We introduce the notion of "read-awareness" to log-structured merge trees, which allows hot objects to be pinned to faster storage, achieving better tiering and hot-cold separation of objects. Compared to the standard use of RocksDB on flash in datacenters today, PrismDB's average throughput on heterogeneous storage is 2.3$\times$ faster and its tail latency is more than an order of magnitude better, using hardware than is half the cost

Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049