Wang K , Zhang K , Yuan Y , et al. 
Concurrent analytical query processing with GPUs[J]. 
Proceedings of the VLDB Endowment, 2014, 7(11):1011-1022.

ABSTRACT

GPUs used as dedicated accelerators to process individual query
Sharing GPUs among concurrent queries not supported
Based on the profiling of
- an opensource GPU query engine
- running commonly used singlequery data warehousing workloads,
- utilization of main GPU resources is 25%.
low system throughput

an opensource GPU query engine一个开源的GPU查询引擎。在这上面跑普遍使用的单查询数据仓库负载

proposes concurrent query execution as an solution.
To efficiently share GPUs among concurrent queries for high throughput,
- provide software support
- to control and resolve resource contention
Our solution relies on GPU query scheduling
- device memory swapping policies
- to address this challenge

implemented a prototype system and evaluated it intensively
results confirm the effectiveness and performance advantage of our approach
By executing multiple GPU queries concurrently, system throughput can be improved by up to 55% compared with dedicated processing

1. INTRODUCTION

Multitasking
- a proven practice in computer systems to achieve high resource utilization and system throughput
wide adoption of GPUs for analytical query processing,
mainly used as dedicated co-processors,
- unable to
- support efficient executions of multiple queries concurrently.

data-driven characteristics of
- GPU operations,
- a single query can hardly consume all GPU resources
Dedicated query processing leads to resource underutilization,
- limits the overall performance of the database system
In market-critical applications
- high-performance data warehousing
- multi-client dataflow analysis
large number of users demand query results simultaneously
volume of data to be processed keeps increasing,
- essential for user queries to make continuous progress
- so that
- new results can be generated constantly to
- satisfy the goal of interactive analysis.
- The lack of concurrent querying capability restricts
  the adoption of GPU databases in these application fields.

dedicated usage of GPUs still needed for latency-
critical queries to ensure performance isolation
databases must support
- concurrent multi-query execution
- to maximize the throughput of non-latency-sensitive queries

对延迟敏感的查询，啥叫去保证性能隔离啊？？

This consolidated usage of GPU resources enhances system efficiency and functionalities, but it makes the design of query execution engine more challenging.

啥叫This consolidated usage of GPU resources

For maximal performance,each user query tends to reserve a large amount of GPU resources.
Unlike CPUs
- OS supports fine-grained context switches
- virtual memory abstractions for resource sharing,
current GPU hardware and system software provide
- none of these interfaces
- for database resource management

GPU任务不能被强占

GPU tasks cannot be preempted once started;
- on-demand data loading is not supported during task execution;

数据只能预先搞嘛？？

automatic data swapping service is also missing when the device memory undergoes pressure

without efficient coordination by the database,
multiple GPU queries attempting to execute simultaneously cause
- low resource usage,
- system thrashing,
- even query abortions,
- degrade, overall system performance