150 citations · 1.3k across the 73 of their papers we have counts for
16 papers · 1 filter
A Compiler Framework for Optimizing Dynamic Parallelism on GPUs
Mhd Ghaith Olabi, Juan Gómez Luna, Onur Mutlu +2
Dynamic parallelism on GPUs allows GPU threads to dynamically launch other GPU threads. It is useful in applications with nested parallelism, particularly where the amount of neste…
ETICA: Efficient Two-Level I/O Caching Architecture for Virtualized Platforms
Saba Ahmadian, Reza Salkhordeh, Onur Mutlu +1
In this paper, we propose an Efficient Two-Level I/O Caching Architecture (ETICA) for virtualized platforms that can significantly improve I/O latency, endurance, and cost (in term…
IChannels: Exploiting Current Management Mechanisms to Create Covert Channels in Modern Processors
Jawad Haj-Yahya, Jeremie S. Kim, A. Giray Yaglikci +5
To operate efficiently across a wide range of workloads with varying power requirements, a modern processor applies different current management mechanisms, which briefly throttle…
Accelerating B-spline Interpolation on GPUs: Application to Medical Image Registration
Orestis Zachariadis, Andrea Teatini, Nitin Satpute +4
Background and Objective. B-spline interpolation (BSI) is a popular technique in the context of medical imaging due to its adaptability and robustness in 3D object modeling. A fiel…
SMASH: Co-designing Software Compression and Hardware-Accelerated Indexing for Efficient Sparse Matrix Operations
Konstantinos Kanellopoulos, Nandita Vijaykumar, Christina Giannoula +6
Important workloads, such as machine learning and graph analytics applications, heavily involve sparse linear algebra operations. These operations use sparse matrix compression as…
EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM
Skanda Koppula, Lois Orosa, Abdullah Giray Yağlıkçı +4
The effectiveness of deep neural networks (DNN) in vision, speech, and language processing has prompted a tremendous demand for energy-efficient high-performance DNN inference syst…