NDLI: DASX: Hardware Accelerator for Software Data Structures

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DASX: Hardware Accelerator for Software Data Structures

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DASX: Hardware Accelerator for Software Data Structures

Content Provider	ACM Digital Library
Author	Shriraman, Arrvindh Srinivasan, Vijayalakshmi Kumar, Snehasish Vedula, Naveen
Abstract	Recent research [3,37,38] has proposed compute accelerators to address the energy efficiency challenge. While these compute accelerators specialize and improve the compute efficiency, they have tended to rely on address-based load/store memory interfaces that closely resemble a traditional processor core. The address-based load/store interface is particularly challenging in data-centric applications that tend to access different software data structures. While accelerators optimize the compute section, the address-based interface leads to wasteful instructions and low memory level parallelism (MLP). We study the benefits of raising the abstraction of the memory interface to data structures. We propose DASX (Data Structure Accelerator), a specialized state machine for data fetch that enables compute accelerators to efficiently access data structure elements in iterative program regions. DASX enables the compute accelerators to employ data structure based memory operations and relieves the compute unit from having to generate addresses for each individual object. DASX exploits knowledge of the program's iteration to i) run ahead of the compute units and gather data objects for the compute unit (i.e., compute unit memory operations do not encounter cache misses) and ii) throttle the fetch rate, adaptively tile the dataset based on the locality characteristics and guarantee cache residency. We demonstrate accelerators for three types of data structures, Vector, Key-Value (Hash) maps, and BTrees. We demonstrate the benefits of DASX on data-centric applications which have varied compute kernels but access few regular data structures. DASX achieves higher energy efficiency by eliminating data structure instructions and enabling energy efficient compute accelerators to efficiently access the data elements. We demonstrate that DASX can achieve 4.4x the performance of a multicore system by discovering more parallelism from the data structure.
Starting Page	361
Ending Page	372
Page Count	12
File Format	PDF
ISBN	9781450335591
DOI	10.1145/2751205.2751231
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-08
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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