task: added abstract and moved to a separate file

content/authors/aldrin.montana/_index.md

@@ -37,17 +37,11 @@ social:
 
 # Important: use this to enable a list of pictures for all keynote pictures on the keynote speaker page.
 user_groups:
-- Keynote Speaker
+- Computaional I/O Stack Workshop Speakers
 ---
-**Query processing for a computational storage system**
-
-**Abstract:** __TODO__
-
-**Bio:** Aldrin is a PhD candidate at the University of California, Santa Cruz. He
-is advised by Peter Alvaro and also works with Josh Stuart, Jeff LeFevre, and
-Carlos Maltzahn. His research interests primarily include bioinformatics, data
-management systems, and storage systems. His current research focuses on distributing
-query plans across computational devices in a distributed system where the devices are
-organized hierarchically and may have a wide variety of device characteristics.
-
-**Contact:** Aldrin Montana; e-mail: [email protected]
+Aldrin is a PhD candidate at the University of California, Santa Cruz. He is advised by
+Peter Alvaro and also works with Josh Stuart, Jeff LeFevre, and Carlos Maltzahn. His
+research interests primarily include bioinformatics, data management systems, and storage
+systems. His current research focuses on distributing query plans across computational
+devices in a distributed system where the devices are organized hierarchically and may
+have a wide variety of device characteristics.

content/talks/20230817/aldrin.montana/index.md

@@ -0,0 +1,34 @@
+---
+title: Query processing for a computational storage system
+subtitle: "[Computational I/O Stack Workshop, August 17, 2023](/post/20230718-aug17/)"
+date: 2023-08-16
+authors: [aldrin.montana]
+image:
+  focal_point: 'top'
+---
+
+Existing research in computational storage primarily explores pushdown of a single query
+operator or its supporting functions from a database management system. In these cases,
+the portion of a query that can be pushed down is static, the execution of the kernel is
+device-specific, and the benefits of computational storage only exist between the database
+system and the compuational storage devices (CS devices) it is directly connected to.
+However, an optimal, static partitioning is likely to change whenever workload
+characteristics shift, for varying device characteristics, and for evolving
+characteristics of the storage system.
+
+A storage system grows when new, heterogeneous storage devices are added to the storage
+hierarchy. As characteristics of these devices evolve and they gain more compute
+resources, it will be desirable (and necessary) to deploy heterogeneous data processing
+and storage engines that are well designed for device-specific characteristics or
+data-specific modeling and storage. I call a storage system designed for this complexity a
+computational storage system (CS system).
+
+We are working towards dynamic, just-in-time partitioning of computation to utilize the
+extra compute resources available in a CS system. In this talk, I will describe how we
+approach just-in-time partitioning of computation by decomposing a logical query plan
+(initial query plan) into portions (subplans) that can be distributed. On any given CS
+device, a subplan is processed locally, translated to a physical query plan, then the
+physical query plan is executed. Execution of the physical query plan may be partial
+(best-effort), and the portions that were not executed are propagated with the query
+results so that CS devices higher in the data access path may, collectively, eventually
+execute the initial query plan.