NDLI: ArchRanker:a ranking approach to design space exploration

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Volume 42, Issue 5, December 2014

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Unifying on-chip and inter-node switching within the Anton 2 network

Avoiding core's DUE & SDC via acoustic wave detectors and tailored error containment and recovery

The Direct-to-Data (D2D) cache

Fine-grain task aggregation and coordination on GPUs

Efficient digital neurons for large scale cortical architectures

Memory persistency

Towards energy proportionality for large-scale latency-critical workloads

Row-buffer decoupling

Architecture implications of pads as a scarce resource

The CHERI capability model

General-purpose code acceleration with limited-precision analog computation

A reconfigurable fabric for accelerating large-scale datacenter services

Enabling preemptive multiprogramming on GPUs

An examination of the architecture and system-level tradeoffs of employing steep slope devices in 3D CMPs

Reducing access latency of MLC PCMs through line striping

Increasing off-chip bandwidth in multi-core processors with switchable pins

The dirty-block index

Single-graph multiple flows

Optimizing virtual machine consolidation performance on NUMA server architecture for cloud workloads

Flipping bits in memory without accessing them

A low power and reliable charge pump design for phase change memories

Eliminating redundant fragment shader executions on a mobile GPU via hardware memoization

Real-world design and evaluation of compiler-managed GPU redundant multithreading

Harnessing ISA diversity

Going vertical in memory management

Replay debugging

Improving the energy efficiency of big cores

Volume 42, Issue 2, May 2014

Volume 42, Issue 1, March 2014

ArchRanker:a ranking approach to design space exploration

Content Provider	ACM Digital Library
Author	Chen, Tianshi Tang, Ke Zhou, Zhi-Hua Xu, Zhiwei Chen, Yunji Guo, Qi Temam, Olivier
Abstract	Architectural Design Space Exploration (DSE) is a notoriously difficult problem due to the exponentially large size of the design space and long simulation times. Previously, many studies proposed to formulate DSE as a regression problem which predicts architecture responses (e.g., time, power) of a given architectural configuration. Several of these techniques achieve high accuracy, though often at the cost of significant simulation time for training the regression models. We argue that the information the architect mostly needs during the DSE process is whether a given configuration will perform better than another one in the presences of design constraints, or better than any other one seen so far, rather than precisely estimating the performance of that configuration. Based on this observation, we propose a novel rankingbased approach to DSE where we train a model to predict which of two architecture configurations will perform best. We show that, not only this ranking model more accurately predicts the relative merit of two architecture configurations than an ANN-based state-of-the-art regression model, but also that it requires much fewer training simulations to achieve the same accuracy, or that it can be used for and is even better at quantifying the performance gap between two configurations We implement the framework for training and using this model, called ArchRanker, and we evaluate it on several DSE scenarios (unicore/multicore design spaces, and both time and power performance metrics). We try to emulate as closely as possible the DSE process by creating constraint-based scenarios, or an iterative DSE process. We find that ArchRanker makes 29:68% to 54:43% fewer incorrect predictions on pairwise relative merit of configurations (tested with 79,800 configuration pairs) than an ANN-based regression model across all DSE scenarios considered (values averaged over all benchmarks for each scenario). We also find that, to achieve the same accuracy as ArchRanker, the ANN often requires three times more training simulations
Starting Page	85
Ending Page	96
Page Count	12
File Format	PDF
ISSN	01635964
DOI	10.1145/2678373.2665688
Journal	ACM SIGARCH Computer Architecture News (CARN)
Volume Number	42
Issue Number	3
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1981-04-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article

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