ﻻ يوجد ملخص باللغة العربية
Todays data analytics frameworks are compute-centric, with analytics execution almost entirely dependent on the pre-determined physical structure of the high-level computation. Relegating intermediate data to a second class entity in this manner hurts flexibility, performance, and efficiency. We present Whiz, a new analytics framework that cleanly separates computation from intermediate data. It enables runtime visibility into data via programmable monitoring, and data-driven computation (where intermediate data values drive when/what computation runs) via an event abstraction. Experiments with a Whiz prototype on a large cluster using batch, streaming, and graph analytics workloads show that its performance is 1.3-2x better than state-of-the-art.
FPGA-based data processing in datacenters is increasing in popularity due to the demands of modern workloads and the ensuing necessity for specialization in hardware. Driven by this trend, vendors are rapidly adapting reconfigurable devices to suit d
Understanding and tuning the performance of extreme-scale parallel computing systems demands a streaming approach due to the computational cost of applying offline algorithms to vast amounts of performance log data. Analyzing large streaming data is
Data analytics applications transform raw input data into analytics-specific data structures before performing analytics. Unfortunately, such data ingestion step is often more expensive than analytics. In addition, various types of NVRAM devices are
Storage and memory systems for modern data analytics are heavily layered, managing shared persistent data, cached data, and non-shared execution data in separate systems such as distributed file system like HDFS, in-memory file system like Alluxio an
Federated Learning allows remote centralized server training models without to access the data stored in distributed (edge) devices. Most work assume the data generated from edge devices is identically and independently sampled from a common populati