Communications of the ACM
Latest Research
From Communications of the ACM
Technical Perspective: Graphs, Betweenness Centrality, and the GPU
"Accelerating GPU Betweenness Centrality" by McLaughlin and Bader ably addresses the challenges to authors of efficient graph implementations in the important context...
From Communications of the ACM
Accelerating GPU Betweenness Centrality
We present a hybrid GPU implementation that provides good performance on graphs of arbitrary structure rather than just scale-free graphs as was done previously...
From Communications of the ACM
Technical Perspective: Measuring Optimization Potential with Coz
In "Coz: Finding Code that Counts with Causal Profiling," Curtsinger and Berger describe causal profiling, which tell programmers exactly how much speed-up bang...
From Communications of the ACM
Coz: Finding Code that Counts with Causal Profiling
This paper introduces causal profiling. Unlike past profiling approaches, causal profiling indicates exactly where programmers should focus their optimization efforts...
From Communications of the ACM
Technical Perspective: A Gloomy Look at the Integrity of Hardware
"Exploiting the Analog Properties of Digital Circuits for Malicious Hardware," by Kaiyuan Yang, et al., assumes semiconductor foundries (and others in chip fabrication)...
From Communications of the ACM
Exploiting the Analog Properties of Digital Circuits For Malicious Hardware
We show how a fabrication-time attacker can leverage analog circuits to create a hardware attack that is small and stealthy.
From Communications of the ACM
Technical Perspective: Unexpected Connections
The inherent scalability of an interface is the focus of "The Scalable Commutativity Rule" by Austin T. Clements, et al.
From Communications of the ACM
The Scalable Commutativity Rule: Designing Scalable Software For Multicore Processors
This paper introduces an interface-driven approach to building scalable software.
From Communications of the ACM
Technical Perspective: Ironfleet Simplifies Proving Safety and Liveness Properties
"IronFleet: Proving Safety and Liveness of Practical Distributed Systems," by Chris Hawblitzel, et al., describes mechanically checked proofs for two non-trivial...
From Communications of the ACM
Ironfleet: Proving Safety and Liveness of Practical Distributed Systems
We demonstrate the methodology on a complex implementation of a Paxos-based replicated state machine library and a lease-based sharded key-value store. With our...
From Communications of the ACM
Technical Perspective: What Led Computer Vision to Deep Learning?
We are in the middle of the third wave of interest in artificial neural networks as the leading paradigm for machine learning. "ImageNet Classification with Deep...
From Communications of the ACM
ImageNet Classification with Deep Convolutional Neural Networks
In the 1980s backpropagation did not live up to the very high expectations of its advocates. Twenty years later, we know what went wrong: for deep neural networks...
From Communications of the ACM
Technical Perspective: The Power of Wi-Fi to Deliver Power
The authors of "Powering the Next Billion Devices with Wi-Fi" turn the problem of powering wireless sensor networks on its head. Instead of focusing on energy harvesting...
From Communications of the ACM
Powering the Next Billion Devices with Wi-Fi
We present the first power over Wi-Fi system that delivers power to low-power sensors and devices and works with existing Wi-Fi chipsets.
From Communications of the ACM
Technical Perspective: FPGA Compute Acceleration Is First About Energy Efficiency
"A Reconfigurable Fabric for Accelerating Large-Scale Datacenter Services" presents a research deployment of Field Programmable Gate Arrays (FPGAs) in a Microsoft...
From Communications of the ACM
A Reconfigurable Fabric For Accelerating Large-Scale Datacenter Services
We describe a medium-scale deployment of a composable, reconfigurable hardware fabric on a bed of 1,632 servers, and measure its effectiveness in accelerating the...
From Communications of the ACM
Technical Perspective: If I Could Only Design One Circuit . . .
"DianNao Family: Energy-Efficient Hardware Accelerators for Machine Learning" shows a deep understanding of both neural net implementations and the issues in computer...
From Communications of the ACM
Diannao Family: Energy-Efficient Hardware Accelerators For Machine Learning
We introduce a series of hardware accelerators (i.e., the DianNao family) designed for Machine Learning (especially neural networks), with a special emphasis on...
From Communications of the ACM
Technical Perspective: Toward Reliable Programming For Unreliable Hardware
"Verifying Quantitative Reliability for Programs that Execute on Unreliable Hardware" by Carbin et al. addresses challenges related to a bug, how likely it is to...
From Communications of the ACM