Computing Big-data Applications in Flash

Speaker:        Shuotao XU
                MIT

Title:          "Computing Big-data Applications in Flash"

Date:           Monday, 12 October 2020

Time:           10:00am - 11:00am


Zoom web link:
https://hkust.zoom.us/j/94987739849?pwd=YlVuZVMzQ25weXpFRjZ3YUo5NG4vZz09

Meeting ID:     949 8773 9849
Passcode:       121020

Abstract:

In our "Big-data Era", vast amount of data is being collected continuously
from interactive social networks, eCommerce, web searches, and various
sensors. The economic value of this "big-data" is highly dependent on how
cost-effectively one can store and analyze it. In the "elastic cloud",
where application servers and storage servers are connected by relatively
a slow network, latency-sensitive applications, such as internet searches
and social networking employ a middle-layer of DRAM-based "caching
services" to overcome the long-latency and coarse-grain of storage
accesses. For throughput-bound applications, such as analytics on
terabytes/petabytes datasets, application servers push computations into
storage servers to reduce data movement over the network. Both solutions
require large number CPUs and DRAM, which are expensive in terms of
equipment cost, area and power. We present an alternative solution using
flash storage with hardware accelerators to make big-data applications
more affordable.

In this talk I will describe two novel hardware-accelerated flash-based
architectures we have built: BlueCache, a scalable flash-based key-value
cache for data-centers, and AQUOMAN, an in-storage analytic-query
offloading machine for SQL analytics. Both systems reduce the CPU and DRAM
resource requirements significantly without sacrificing applications
performance.


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Biography:

Shuotao Xu is a PhD Candidate of Computer Science at Massachusetts
Institute of Technology, advised by Professor Arvind. His research
interests are novel in-storage system architectures for accelerating
big-data applications, and designing application-specific accelerators
using FPGAs.