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Michael Laß

Contact
 Michael Laß

Paderborn Center for Parallel Computing (PC2)

Member - Research Assistant - Research Associate

High-Performance IT Systems

Member - Research Student

Phone:
+49 5251 60-1722
Fax:
+49 5251 60-1714
Office:
O3.152
Web:
Web(external):
Visitor:
Pohlweg 51
33098 Paderborn

Publications


Open list in Research Information System

2019

A General Algorithm to Calculate the Inverse Principal p-th Root of Symmetric Positive Definite Matrices

D. Richters, M. Lass, A. Walther, C. Plessl, T. Kühne, Communications in Computational Physics (2019), 25(2), pp. 564-585

We address the general mathematical problem of computing the inverse p-th root of a given matrix in an efficient way. A new method to construct iteration functions that allow calculating arbitrary p-th roots and their inverses of symmetric positive definite matrices is presented. We show that the order of convergence is at least quadratic and that adaptively adjusting a parameter q always leads to an even faster convergence. In this way, a better performance than with previously known iteration schemes is achieved. The efficiency of the iterative functions is demonstrated for various matrices with different densities, condition numbers and spectral radii.


Accurate Sampling with Noisy Forces from Approximate Computing

V. Rengaraj, M. Lass, C. Plessl, T. Kühne, in: arXiv:1907.08497, 2019

In scientific computing, the acceleration of atomistic computer simulations by means of custom hardware is finding ever growing application. A major limitation, however, is that the high efficiency in terms of performance and low power consumption entails the massive usage of low-precision computing units. Here, based on the approximate computing paradigm, we present an algorithmic method to rigorously compensate for numerical inaccuracies due to low-accuracy arithmetic operations, yet still obtaining exact expectation values using a properly modified Langevin-type equation.


2018

A Massively Parallel Algorithm for the Approximate Calculation of Inverse p-th Roots of Large Sparse Matrices

M. Lass, S. Mohr, H. Wiebeler, T. Kühne, C. Plessl, in: Proc. Platform for Advanced Scientific Computing (PASC) Conference, ACM, 2018

We present the submatrix method, a highly parallelizable method for the approximate calculation of inverse p-th roots of large sparse symmetric matrices which are required in different scientific applications. Following the idea of Approximate Computing, we allow imprecision in the final result in order to utilize the sparsity of the input matrix and to allow massively parallel execution. For an n x n matrix, the proposed algorithm allows to distribute the calculations over n nodes with only little communication overhead. The result matrix exhibits the same sparsity pattern as the input matrix, allowing for efficient reuse of allocated data structures. We evaluate the algorithm with respect to the error that it introduces into calculated results, as well as its performance and scalability. We demonstrate that the error is relatively limited for well-conditioned matrices and that results are still valuable for error-resilient applications like preconditioning even for ill-conditioned matrices. We discuss the execution time and scaling of the algorithm on a theoretical level and present a distributed implementation of the algorithm using MPI and OpenMP. We demonstrate the scalability of this implementation by running it on a high-performance compute cluster comprised of 1024 CPU cores, showing a speedup of 665x compared to single-threaded execution.


Using Approximate Computing for the Calculation of Inverse Matrix p-th Roots

M. Lass, T. Kühne, C. Plessl, Embedded Systems Letters (2018), 10(2), pp. 33-36

Approximate computing has shown to provide new ways to improve performance and power consumption of error-resilient applications. While many of these applications can be found in image processing, data classification or machine learning, we demonstrate its suitability to a problem from scientific computing. Utilizing the self-correcting behavior of iterative algorithms, we show that approximate computing can be applied to the calculation of inverse matrix p-th roots which are required in many applications in scientific computing. Results show great opportunities to reduce the computational effort and bandwidth required for the execution of the discussed algorithm, especially when targeting special accelerator hardware.


2017

Efficient Branch and Bound on FPGAs Using Work Stealing and Instance-Specific Designs

H. Riebler, M. Lass, R. Mittendorf, T. Löcke, C. Plessl, ACM Transactions on Reconfigurable Technology and Systems (TRETS) (2017), 10(3), pp. 24:1-24:23

Branch and bound (B&B) algorithms structure the search space as a tree and eliminate infeasible solutions early by pruning subtrees that cannot lead to a valid or optimal solution. Custom hardware designs significantly accelerate the execution of these algorithms. In this article, we demonstrate a high-performance B&B implementation on FPGAs. First, we identify general elements of B&B algorithms and describe their implementation as a finite state machine. Then, we introduce workers that autonomously cooperate using work stealing to allow parallel execution and full utilization of the target FPGA. Finally, we explore advantages of instance-specific designs that target a specific problem instance to improve performance. We evaluate our concepts by applying them to a branch and bound problem, the reconstruction of corrupted AES keys obtained from cold-boot attacks. The evaluation shows that our work stealing approach is scalable with the available resources and provides speedups proportional to the number of workers. Instance-specific designs allow us to achieve an overall speedup of 47 × compared to the fastest implementation of AES key reconstruction so far. Finally, we demonstrate how instance-specific designs can be generated just-in-time such that the provided speedups outweigh the additional time required for design synthesis.


2016

Confidentiality and Authenticity for Distributed Version Control Systems - A Mercurial Extension

M. Lass, D. Leibenger, C. Sorge, in: Proc. 41st Conference on Local Computer Networks (LCN), IEEE, 2016

Version Control Systems (VCS) are a valuable tool for software development and document management. Both client/server and distributed (Peer-to-Peer) models exist, with the latter (e.g., Git and Mercurial) becoming increasingly popular. Their distributed nature introduces complications, especially concerning security: it is hard to control the dissemination of contents stored in distributed VCS as they rely on replication of complete repositories to any involved user. We overcome this issue by designing and implementing a concept for cryptography-enforced access control which is transparent to the user. Use of field-tested schemes (end-to-end encryption, digital signatures) allows for strong security, while adoption of convergent encryption and content-defined chunking retains storage efficiency. The concept is seamlessly integrated into Mercurial---respecting its distributed storage concept---to ensure practical usability and compatibility to existing deployments.


Using Approximate Computing in Scientific Codes

M. Lass, T. Kühne, C. Plessl, in: Workshop on Approximate Computing (AC), 2016


2015

Localization and Analysis of Code Paths Suitable for Acceleration using Approximate Computing

M. Lass, Master's thesis, Paderborn University, 2015

Demands for computational power and energy efficiency of computing devices are steadily increasing. At the same time, following classic methods to increase speed and reduce energy consumption of these devices becomes increasingly difficult, bringing alternative methods into focus. One of these methods is approximate computing which utilizes the fact that small errors in computations are acceptable in many applications in order to allow acceleration of these computations or to increase energy efficiency. This thesis develops elements of a workflow that can be followed to apply approximate computing to existing applications. It proposes a novel heuristic approach to the localization of code paths that are suitable to approximate computing based on findings in recent research. Additionally, an approach to identification of approximable instructions within these code paths is proposed and used to implement simulation of approximation. The parts of the workflow are implemented with the goal to lay the foundation for a partly automated toolflow. Evaluation of the developed techniques shows that the proposed methods can help providing a convenient workflow, facilitating the first steps into the application of approximate computing.


2013

Sichere Speicherung vertraulicher Daten in verteilten Versionskontrollsystemen

M. Lass, Bachelor's thesis, Paderborn University, 2013

Distributed revision control is widespread throughout the software industry. Systems like git and mercurial gained a lot of users over the last years and started to supersede central systems like Subversion or CVS in some projects. While restricting access to those central systems is basically possible, it is difficult to control the propagation of contents in a distributed revision control system because every user has a local copy of the whole repository. In this thesis a concept is developed and implemented that allows secure storage of confidential data in a distributed revision control system and enables users to manage read and write permissions on single confidential files. Therefore different cryptographic methods are used, such as asymmetric encryption, digital signatures and convergent encryption. These techniques are applied in a manner that fits the special requirements of a revision control system and allows a space efficient storage of changes to the encrypted files.


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