97 citations · 493 across the 23 of their papers we have counts for
24 papers
RobotPerf: An Open-Source, Vendor-Agnostic, Benchmarking Suite for Evaluating Robotics Computing System Performance
Víctor Mayoral-Vilches, Jason Jabbour, Yu-Shun Hsiao +16
We introduce RobotPerf, a vendor-agnostic benchmarking suite designed to evaluate robotics computing performance across a diverse range of hardware platforms using ROS 2 as its com…
ACRoBat: Optimizing Auto-batching of Dynamic Deep Learning at Compile Time
Pratik Fegade, Tianqi Chen, Phillip B. Gibbons +1
Dynamic control flow is an important technique often used to design expressive and efficient deep learning computations for applications such as text parsing, machine translation,…
ED-Batch: Efficient Automatic Batching of Dynamic Neural Networks via Learned Finite State Machines
Siyuan Chen, Pratik Fegade, Tianqi Chen +2
Batching has a fundamental influence on the efficiency of deep neural network (DNN) execution. However, for dynamic DNNs, efficient batching is particularly challenging as the data…
PIM-tree: A Skew-resistant Index for Processing-in-Memory
Hongbo Kang, Yiwei Zhao, Guy E. Blelloch +4
The performance of today's in-memory indexes is bottlenecked by the memory latency/bandwidth wall. Processing-in-memory (PIM) is an emerging approach that potentially mitigates thi…
Spatial Locality and Granularity Change in Caching
Nathan Beckmann, Phillip B Gibbons, Charles McGuffey
Caches exploit temporal and spatial locality to allow a small memory to provide fast access to data stored in large, slow memory. The temporal aspect of locality is extremely well…
The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding
Pratik Fegade, Tianqi Chen, Phillip B. Gibbons +1
There is often variation in the shape and size of input data used for deep learning. In many cases, such data can be represented using tensors with non-uniform shapes, or ragged te…
MetaSys: A Practical Open-Source Metadata Management System to Implement and Evaluate Cross-Layer Optimizations
Nandita Vijaykumar, Ataberk Olgun, Konstantinos Kanellopoulos +5
This paper introduces the first open-source FPGA-based infrastructure, MetaSys, with a prototype in a RISC-V core, to enable the rapid implementation and evaluation of a wide range…
Cortex: A Compiler for Recursive Deep Learning Models
Pratik Fegade, Tianqi Chen, Phillip B. Gibbons +1
Optimizing deep learning models is generally performed in two steps: (i) high-level graph optimizations such as kernel fusion and (ii) low level kernel optimizations such as those…
DriftSurf: A Risk-competitive Learning Algorithm under Concept Drift
Ashraf Tahmasbi, Ellango Jothimurugesan, Srikanta Tirthapura +1
When learning from streaming data, a change in the data distribution, also known as concept drift, can render a previously-learned model inaccurate and require training a new model…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Sage: Parallel Semi-Asymmetric Graph Algorithms for NVRAMs
Laxman Dhulipala, Charlie McGuffey, Hongbo Kang +4
Non-volatile main memory (NVRAM) technologies provide an attractive set of features for large-scale graph analytics, including byte-addressability, low idle power, and improved mem…
The Non-IID Data Quagmire of Decentralized Machine Learning
Kevin Hsieh, Amar Phanishayee, Onur Mutlu +1
Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations. Such datasets pose a signific…
MLSys: The New Frontier of Machine Learning Systems
Alexander Ratner, Dan Alistarh, Gustavo Alonso +66
Machine learning (ML) techniques are enjoying rapidly increasing adoption. However, designing and implementing the systems that support ML models in real-world deployments remains…
The Parallel Persistent Memory Model
Guy E. Blelloch, Phillip B. Gibbons, Yan Gu +2
We consider a parallel computational model that consists of processors, each with a fast local ephemeral memory of limited size, and sharing a large persistent memory. The mode…
PipeDream: Fast and Efficient Pipeline Parallel DNN Training
Aaron Harlap, Deepak Narayanan, Amar Phanishayee +4
PipeDream is a Deep Neural Network(DNN) training system for GPUs that parallelizes computation by pipelining execution across multiple machines. Its pipeline parallel computing mod…
RowClone: Accelerating Data Movement and Initialization Using DRAM
Vivek Seshadri, Yoongu Kim, Chris Fallin +8
In existing systems, to perform any bulk data movement operation (copy or initialization), the data has to first be read into the on-chip processor, all the way into the L1 cache,…
Decoupling GPU Programming Models from Resource Management for Enhanced Programming Ease, Portability, and Performance
Nandita Vijaykumar, Kevin Hsieh, Gennady Pekhimenko +6
The application resource specification--a static specification of several parameters such as the number of threads and the scratchpad memory usage per thread block--forms a critica…
MLtuner: System Support for Automatic Machine Learning Tuning
Henggang Cui, Gregory R. Ganger, Phillip B. Gibbons
MLtuner automatically tunes settings for training tunables (such as the learning rate, the momentum, the mini-batch size, and the data staleness bound) that have a significant impa…
Zorua: Enhancing Programming Ease, Portability, and Performance in GPUs by Decoupling Programming Models from Resource Management
Nandita Vijaykumar, Kevin Hsieh, Gennady Pekhimenko +5
The application resource specification--a static specification of several parameters such as the number of threads and the scratchpad memory usage per thread block--forms a critica…
Focus: Querying Large Video Datasets with Low Latency and Low Cost
Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodik +4
Large volumes of videos are continuously recorded from cameras deployed for traffic control and surveillance with the goal of answering "after the fact" queries: identify video fra…
Implicit Decomposition for Write-Efficient Connectivity Algorithms
Naama Ben-David, Guy E. Blelloch, Jeremy T. Fineman +4
The future of main memory appears to lie in the direction of new technologies that provide strong capacity-to-performance ratios, but have write operations that are much more expen…
Buddy-RAM: Improving the Performance and Efficiency of Bulk Bitwise Operations Using DRAM
Vivek Seshadri, Donghyuk Lee, Thomas Mullins +7
Bitwise operations are an important component of modern day programming. Many widely-used data structures (e.g., bitmap indices in databases) rely on fast bitwise operations on lar…
Sorting with Asymmetric Read and Write Costs
Guy E. Blelloch, Jeremy T. Fineman, Phillip B. Gibbons +2
Emerging memory technologies have a significant gap between the cost, both in time and in energy, of writing to memory versus reading from memory. In this paper we present models a…
Efficient Algorithms with Asymmetric Read and Write Costs
Guy E. Blelloch, Jeremy T. Fineman, Phillip B. Gibbons +2
In several emerging technologies for computer memory (main memory), the cost of reading is significantly cheaper than the cost of writing. Such asymmetry in memory costs poses a fu…