John Ruttenberg – Reservoir Labs, Inc.

13 Jan 2021

Static Versioning in the Polyhedral Model

Adithya Dattatri, Benoit Meister

Source: 11th International Workshop on Polyhedral Compilation Tec...

We present an approach to enhancing the optimization process in a polyhedral compiler by introducing compile-time versioning, i.e., the production…

High Performance Compilers R-Stream®

1 Dec 2020

Trading off Latency for Memory Bandwidth in Spatial Architectures using the Polyhedral Model

Benoit Meister

Source: 2020 International Conference on High Performance Computi...

We present a fast algorithm for transforming loop nests represented in the polyhedral model, which have been placed onto a…

High Performance Compilers R-Stream®

9 Nov 2020

Computing Bottleneck Structures at Scale for High-Precision Network Performance Analysis

Noah Amsel, Jordi Ros-Giralt, | +5 More

Source: 2020 IEEE/ACM Innovating the Network for Data-Intensive S...

The Theory of Bottleneck Structures is a recently developed framework for studying the performance of data networks. It describes how…

Networking

22 Sep 2020

Multiscale Data Analysis Using Binning, Tensor Decompositions, and Backtracking

Dimitri Leggas, Thomas S. Henr | +5 More

Source: IEEE High Performance Extreme Computing Conference (HPEC)...

Large data sets can contain patterns at multiple scales (spatial, temporal, etc.). In practice, it is useful for data exploration…

Data Analytics ENSIGN®

22 Sep 2020

Automatic Mapping and Optimization to Kokkos with Polyhedral Compilation

Muthu Baskaran, Charles Jin, B | +5 More

Source: High Performance Extreme Computing Conference (HPEC) 2020

In the post-Moore’s Law era, the quest for exascale computing has resulted in diverse hardware architecture trends, including novel custom…

High Performance Compilers R-Stream®

22 Sep 2020

Large–scale Sparse Tensor Decomposition Using a Damped Gauss–Newton Method

Teresa M. Ranadive, Muthu M. Baskaran

Source: IEEE High Performance Extreme Computing Conference (HPEC)...

CANDECOMP/PARAFAC (CP) tensor decomposition is a popular unsupervised machine learning method with numerous applications. This process involves modeling a high–dimensional,…

Data Analytics ENSIGN®

22 Sep 2020

Approximate Inverse Chain Preconditioner: Iteration Count Case Study for Spectral Support Solvers

M. Harper Langston, Pierre-Dav | +5 More

Source: High Performance Extreme Computing Conference (HPEC) 2020

As the growing availability of computational power slows, there has been an increasing reliance on algorithmic advances. However, faster algorithms…

Algorithms

3 Sep 2020

HACCLE: An Ecosystem for Building Secure Multi-Party Computations

Yuyan Bao, Kirshanthan Sundara | +5 More

Source: arXiv:2009.01489v1

Cryptographic techniques have the potential to enable distrusting parties to collaborate in fundamentally new ways, but their practical implementation poses…

High Performance Compilers

8 Jun 2020

On the Bottleneck Structure of Congestion-Controlled Networks

Jordi Ros-Giralt, Sruthi Yella | +5 More

Source: ACM SIGMETRICS 2020, Boston MA; Proc. ACM Meas. Anal. Com...

In this paper, we introduce the Theory of Bottleneck Ordering, a mathematical framework that reveals the bottleneck structure of data…

Algorithms Networking

Static Versioning in the Polyhedral Model

Trading off Latency for Memory Bandwidth in Spatial Architectures using the Polyhedral Model

Computing Bottleneck Structures at Scale for High-Precision Network Performance Analysis

Multiscale Data Analysis Using Binning, Tensor Decompositions, and Backtracking

Automatic Mapping and Optimization to Kokkos with Polyhedral Compilation

Large–scale Sparse Tensor Decomposition Using a Damped Gauss–Newton Method

Approximate Inverse Chain Preconditioner: Iteration Count Case Study for Spectral Support Solvers

HACCLE: An Ecosystem for Building Secure Multi-Party Computations

On the Bottleneck Structure of Congestion-Controlled Networks

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