Finite difference methods fengshui: alignment through a mathematics of arrays

Numerous scientific-computational domains make use of array data. The core computing of the numerical methods and the algorithms involved is related to multi-dimensional array manipulation. Memory layout and the access patterns of that data are crucial to the optimal performance of the array-based computations. As we move towards exascale computing, writing portable code for efficient data parallel computations is increasingly requiring an abstract productive working environment. To that end, we present the design of a framework for optimizing scientific array-based computations, building a case study for a Partial Differential Equations solver. By embedding the Mathematics of Arrays formalism in the Magnolia programming language, we assemble a software stack capable of abstracting the continuous high-level application layer from the discrete formulation of the collective array-based numerical methods and algorithms and the final detailed low-level code. The case study lays the groundwork for achieving optimized memory layout and efficient computations while preserving a stable abstraction layer independent of underlying algorithms and changes in the architecture.Peer ReviewedPostprint (author's final draft

I Can See Clearly Now: Clairvoyant Assertions for Deadlock Checking

Under embargo until: 2023-07-04Static analysers are traditionally used to check various correctness properties of software. In the face of refactorings that can have adverse effects on correctness, developers need to analyse the code after refactoring and possibly revert their changes. Here, we take a different approach: we capture the effect of the Hide Delegate refactoring on programs in the ABS modelling language in terms of the base program, which allows us to predict the correctness of the refactored program. In particular, we focus on deadlock-detection. The actual check is encoded with the help of an additional data structure and assertions. Developers can then attempt to discharge assertions as vacuous with the help of a theorem prover such as KeY. On the one hand, this means that we do not require a specific static analyser nor theorem prover, but rather profit from the strength and advances of modern tool support. On the other hand, developers can choose to rely on existing tests to confirm that no assertion is triggered before executing the actual refactoring. Finally, we argue the correctness of our over-approximation.acceptedVersio

Should we individualize training based on force-velocity profiling to improve physical performance in athletes?

The present study aimed to examine the effectiveness of an individualized training program based on force-velocity (FV) profiling on jumping, sprinting, strength, and power in athletes. Forty national level team sport athletes (20 ± 4years, 83 ± 13 kg) from ice-hockey, handball, and soccer completed a 10-week training intervention. A theoretical optimal squat jump (SJ)-FV-profile was calculated from SJ with five different loads (0, 20, 40, 60, and 80 kg). Based on their initial FV-profile, athletes were randomized to train toward, away, or irrespective (balanced training) of their initial theoretical optimal FV-profile. The training content was matched between groups in terms of set x repetitions but varied in relative loading to target the different aspects of the FV-profile. The athletes performed 10 and 30 m sprints, SJ and countermovement jump (CMJ), 1 repetition maximum (1RM) squat, and a leg-press power test before and after the intervention. There were no significant group differences for any of the performance measures. Trivial to small changes in 1RM squat (2.9%, 4.6%, and 6.5%), 10 m sprint time (1.0%, −0.9%, and −1.7%), 30 m sprint time (0.9%, −0.6%, and −0.4%), CMJ height (4.3%, 3.1%, and 5.7%), SJ height (4.8%, 3.7%, and 5.7%), and leg-press power (6.7%, 4.2%, and 2.9%) were observed in the groups training toward, away, or irrespective of their initial theoretical optimal FV-profile, respectively. Changes toward the optimal SJ-FV-profile were negatively correlated with changes in SJ height (r = −0.49, p < 0.001). Changes in SJ-power were positively related to changes in SJ-height (r = 0.88, p < 0.001) and CMJ-height (r = 0.32, p = 0.044), but unrelated to changes in 10 m (r = −0.02, p = 0.921) and 30 m sprint time (r = −0.01, p = 0.974). The results from this study do not support the efficacy of individualized training based on SJ-FV profiling.publishedVersio

Transformations for Array programming

Masteroppgave i informatikkINF399MAMN-INFMAMN-PRO

Finite difference methods fengshui: alignment through a mathematics of arrays

Numerous scientific-computational domains make use of array data. The core computing of the numerical methods and the algorithms involved is related to multi-dimensional array manipulation. Memory layout and the access patterns of that data are crucial to the optimal performance of the array-based computations. As we move towards exascale computing, writing portable code for efficient data parallel computations is increasingly requiring an abstract productive working environment. To that end, we present the design of a framework for optimizing scientific array-based computations, building a case study for a Partial Differential Equations solver. By embedding the Mathematics of Arrays formalism in the Magnolia programming language, we assemble a software stack capable of abstracting the continuous high-level application layer from the discrete formulation of the collective array-based numerical methods and algorithms and the final detailed low-level code. The case study lays the groundwork for achieving optimized memory layout and efficient computations while preserving a stable abstraction layer independent of underlying algorithms and changes in the architecture.Peer Reviewe

Should we individualize training based on force-velocity profiling to improve physical performance in athletes?

The present study aimed to examine the effectiveness of an individualized training program based on force-velocity (FV) profiling on jumping, sprinting, strength, and power in athletes. Forty national level team sport athletes (20 ± 4years, 83 ± 13 kg) from ice-hockey, handball, and soccer completed a 10-week training intervention. A theoretical optimal squat jump (SJ)-FV-profile was calculated from SJ with five different loads (0, 20, 40, 60, and 80 kg). Based on their initial FV-profile, athletes were randomized to train toward, away, or irrespective (balanced training) of their initial theoretical optimal FV-profile. The training content was matched between groups in terms of set x repetitions but varied in relative loading to target the different aspects of the FV-profile. The athletes performed 10 and 30 m sprints, SJ and countermovement jump (CMJ), 1 repetition maximum (1RM) squat, and a leg-press power test before and after the intervention. There were no significant group differences for any of the performance measures. Trivial to small changes in 1RM squat (2.9%, 4.6%, and 6.5%), 10 m sprint time (1.0%, −0.9%, and −1.7%), 30 m sprint time (0.9%, −0.6%, and −0.4%), CMJ height (4.3%, 3.1%, and 5.7%), SJ height (4.8%, 3.7%, and 5.7%), and leg-press power (6.7%, 4.2%, and 2.9%) were observed in the groups training toward, away, or irrespective of their initial theoretical optimal FV-profile, respectively. Changes toward the optimal SJ-FV-profile were negatively correlated with changes in SJ height (r = −0.49, p < 0.001). Changes in SJ-power were positively related to changes in SJ-height (r = 0.88, p < 0.001) and CMJ-height (r = 0.32, p = 0.044), but unrelated to changes in 10 m (r = −0.02, p = 0.921) and 30 m sprint time (r = −0.01, p = 0.974). The results from this study do not support the efficacy of individualized training based on SJ-FV profiling