arXiv subjects

Mirko Myllykoski

Publications and source records attributed to Mirko Myllykoski.

5 recordsLinked to original sources

arXiv2026

Dynamic Modulated Arc Therapy (DMAT): A Time Aware, Modulation Steered Optimization Framework for Next Generation Radiotherapy Delivery

Background: Conventional VMAT optimization treats delivery time and deliverability as emergent properties of control-point-centric models that ignore finite acceleration and other dynamic limits. As linacs gain axis speed and dose rate, the plan quality-time trade-off must become explicit and steerable. Purpose: To introduce Dynamic Modulated Arc Therapy (DMAT), a time-aware, modulation-steered framework that jointly optimizes dosimetric quality, delivery time, and modulation complexity. Methods: DMAT couples direct machine emulation (axis synchronization, finite acceleration), dynamic modulation control, and clinical metrics used directly as cost functions. A user-selected modulation level (-3 to +3) governs leaf-travel allowance, total MU, aperture complexity, and control-point (CP) density. Plans are generated by progressive-resolution optimization alternating dosimetric with sequencing/deliverability updates, with non-uniform CP redistribution and complexity-reducing post-processing. DMAT was evaluated on head-and-neck, lung SBRT, and prostate SBRT cases using a hypothetical accelerated system (2.5 RPM gantry, 6.25 cm/s MLC, 3000 MU/min). Results: Increasing modulation level raised modulation surrogates (MU/Gy, aperture complexity) and delivery time, with additional CPs concentrated in arc sectors where finer angular resolution was most beneficial. The trade-off was site dependent: head-and-neck gained substantial plan quality, whereas prostate and lung SBRT gained little beyond baseline. Negative levels predictably shortened delivery time at a fixed CP budget, with quantifiable quality loss. Conclusions: DMAT co-optimizes plan quality and modulation complexity under machine-aware timing and explicit user control, making quality-time trade-offs transparent and navigable and supporting time-constrained workflows such as motion management and adaptive radiotherapy.

physics.med-ph↗

arXiv2021

Algorithm 1019: A Task-based Multi-shift QR/QZ Algorithm with Aggressive Early Deflation

The QR algorithm is one of the three phases in the process of computing the eigenvalues and the eigenvectors of a dense nonsymmetric matrix. This paper describes a task-based QR algorithm for reducing an upper Hessenberg matrix to real Schur form. The task-based algorithm also supports generalized eigenvalue problems (QZ algorithm) but this paper concentrates on the standard case. The task-based algorithm adopts previous algorithmic improvements, such as tightly-coupled multi-shifts and Aggressive Early Deflation (AED), and also incorporates several new ideas that significantly improve the performance. This includes, but is not limited to, the elimination of several synchronization points, the dynamic merging of previously separate computational steps, the shortening and the prioritization of the critical path, and experimental GPU support. The task-based implementation is demonstrated to be multiple times faster than multi-threaded LAPACK and ScaLAPACK in both single-node and multi-node configurations on two different machines based on Intel and AMD CPUs. The implementation is built on top of the StarPU runtime system and is part of the open-source StarNEig library.

cs.MS↗

arXiv2020

Parallel Robust Computation of Generalized Eigenvectors of Matrix Pencils

In this paper we consider the problem of computing generalized eigenvectors of a matrix pencil in real Schur form. In exact arithmetic, this problem can be solved using substitution. In practice, substitution is vulnerable to floating-point overflow. The robust solvers xTGEVC in LAPACK prevent overflow by dynamically scaling the eigenvectors. These subroutines are sequential scalar codes which compute the eigenvectors one by one. In this paper we discuss how to derive robust blocked algorithms. The new StarNEig library contains a robust task-parallel solver Zazamoukh which runs on top of StarPU. Our numerical experiments show that Zazamoukh achieves a super-linear speedup compared with DTGEVC for sufficiently large matrices.

cs.MS↗

arXiv2020

Task-based, GPU-accelerated and Robust Library for Solving Dense Nonsymmetric Eigenvalue Problems

In this paper, we present the StarNEig library for solving dense nonsymmetric standard and generalized eigenvalue problems. The library is built on top of the StarPU runtime system and targets both shared and distributed memory machines. Some components of the library have support for GPU acceleration. The library is currently in an early beta state and supports only real matrices. Support for complex matrices is planned for a future release. This paper is aimed at potential users of the library. We describe the design choices and capabilities of the library, and contrast them to existing software such as ScaLAPACK. StarNEig implements a ScaLAPACK compatibility layer which should assist new users in the transition to StarNEig. We demonstrate the performance of the library with a sample of computational experiments.

cs.MS↗

arXiv2019

Introduction to StarNEig -- A Task-based Library for Solving Nonsymmetric Eigenvalue Problems

In this paper, we present the StarNEig library for solving dense non-symmetric (generalized) eigenvalue problems. The library is built on top of the StarPU runtime system and targets both shared and distributed memory machines. Some components of the library support GPUs. The library is currently in an early beta state and only real arithmetic is supported. Support for complex data types is planned for a future release. This paper is aimed for potential users of the library. We describe the design choices and capabilities of the library, and contrast them to existing software such as ScaLAPACK. StarNEig implements a ScaLAPACK compatibility layer that should make it easy for a new user to transition to StarNEig. We demonstrate the performance of the library with a small set of computational experiments.

cs.DC↗