159 citations · 420 across the 16 of their papers we have counts for
19 papers
SKYE: Write-Optimized Key-Value Store with Fine-Grained Control over Persistent Memory Accesses
Soujanya Ponnapalli, Sekwon Lee, Rohan Kadekodi +1
State-of-the-art key-value stores built for persistent memory (PM) provide low latency as they allow application threads to directly access the data on PM and rely on hardware to m…
DINOMO: An Elastic, Scalable, High-Performance Key-Value Store for Disaggregated Persistent Memory (Extended Version)
Sekwon Lee, Soujanya Ponnapalli, Sharad Singhal +3
We present Dinomo, a novel key-value store for disaggregated persistent memory (DPM). Dinomo is the first key-value store for DPM that simultaneously achieves high common-case perf…
Finding and Analyzing Crash-Consistency Bugs in Persistent-Memory File Systems
Hayley LeBlanc, Shankara Pailoor, Isil Dillig +2
We present a study of crash-consistency bugs in persistent-memory (PM) file systems and analyze their implications for file-system design and testing crash consistency. We develop…
TACCL: Guiding Collective Algorithm Synthesis using Communication Sketches
Aashaka Shah, Vijay Chidambaram, Meghan Cowan +6
Machine learning models are increasingly being trained across multiple GPUs and servers. In this setting, data is transferred between GPUs using communication collectives such as A…
Synergy: Resource Sensitive DNN Scheduling in Multi-Tenant Clusters
Jayashree Mohan, Amar Phanishayee, Janardhan Kulkarni +1
Training Deep Neural Networks (DNNs) is a widely popular workload in both enterprises and cloud data centers. Existing schedulers for DNN training consider GPU as the dominant reso…
PAIO: A Software-Defined Storage Data Plane Framework
Ricardo Macedo, Yusuke Tanimura, Jason Haga +3
We propose PAIO, the first general-purpose framework that enables system designers to build custom-made Software-Defined Storage (SDS) data plane stages. It provides the means to i…
Memory Optimization for Deep Networks
Aashaka Shah, Chao-Yuan Wu, Jayashree Mohan +2
Deep learning is slowly, but steadily, hitting a memory bottleneck. While the tensor computation in top-of-the-line GPUs increased by 32x over the last five years, the total availa…
Towards Software-Defined Data Protection: GDPR Compliance at the Storage Layer is Within Reach
Zsolt Istvan, Soujanya Ponnapalli, Vijay Chidambaram
Enforcing data protection and privacy rules within large data processing applications is becoming increasingly important, especially in the light of GDPR and similar regulatory fra…
Analyzing and Mitigating Data Stalls in DNN Training
Jayashree Mohan, Amar Phanishayee, Ashish Raniwala +1
Training Deep Neural Networks (DNNs) is resource-intensive and time-consuming. While prior research has explored many different ways of reducing DNN training time, the impact of in…
GDPR Anti-Patterns: How Design and Operation of Modern Cloud-scale Systems Conflict with GDPR
Supreeth Shastri, Melissa Wasserman, Vijay Chidambaram
In recent years, our society is being plagued by unprecedented levels of privacy and security breaches. To rein in this trend, the European Union, in 2018, introduced a comprehensi…
Understanding and Benchmarking the Impact of GDPR on Database Systems
Supreeth Shastri, Vinay Banakar, Melissa Wasserman +2
The General Data Protection Regulation (GDPR) provides new rights and protections to European people concerning their personal data. We analyze GDPR from a systems perspective, tra…
SplitFS: Reducing Software Overhead in File Systems for Persistent Memory
Rohan Kadekodi, Se Kwon Lee, Sanidhya Kashyap +3
We present SplitFS, a file system for persistent memory (PM) that reduces software overhead significantly compared to state-of-the-art PM file systems. SplitFS presents a novel spl…
RECIPE : Converting Concurrent DRAM Indexes to Persistent-Memory Indexes
Se Kwon Lee, Jayashree Mohan, Sanidhya Kashyap +2
We present Recipe, a principled approach for converting concurrent DRAM indexes into crash-consistent indexes for persistent memory (PM). The main insight behind Recipe is that iso…
Rainblock: Faster Transaction Processing in Public Blockchains
Soujanya Ponnapalli, Aashaka Shah, Amy Tai +4
Public blockchains like Ethereum use Merkle trees to verify transactions received from untrusted servers before applying them to the blockchain. We empirically show that the low th…
Analyzing GDPR Compliance Through the Lens of Privacy Policy
Jayashree Mohan, Melissa Wasserman, Vijay Chidambaram
With the arrival of the European Union's General Data Protection Regulation (GDPR), several companies are making significant changes to their systems to achieve compliance. The cha…
The Seven Sins of Personal-Data Processing Systems under GDPR
Supreeth Shastri, Melissa Wasserman, Vijay Chidambaram
In recent years, our society is being plagued by unprecedented levels of privacy and security breaches. To rein in this trend, the European Union, in 2018, introduced a comprehensi…
Analyzing the Impact of GDPR on Storage Systems
Aashaka Shah, Vinay Banakar, Supreeth Shastri +2
The recently introduced General Data Protection Regulation (GDPR) is forcing several companies to make significant changes to their systems to achieve compliance. Motivated by the…
Finding Crash-Consistency Bugs with Bounded Black-Box Crash Testing
Jayashree Mohan, Ashlie Martinez, Soujanya Ponnapalli +2
We present a new approach to testing file-system crash consistency: bounded black-box crash testing (B3). B3 tests the file system in a black-box manner using workloads of file-sys…
Analyzing IO Amplification in Linux File Systems
Jayashree Mohan, Rohan Kadekodi, Vijay Chidambaram
We present the first systematic analysis of read, write, and space amplification in Linux file systems. While many researchers are tackling write amplification in key-value stores,…