TaskInsight: Understanding Task Schedules Effects on Memory and Performance

Recent scheduling heuristics for task-based applications have managed to improve their by taking into account memory-related properties such as data locality and cache sharing. However, there is still a general lack of tools that can provide insights into why, and where, different schedulers improve memory behavior, and how this is related to the applications' performance. To address this, we present TaskInsight, a technique to characterize the memory behavior of different task schedulers through the analysis of data reuse between tasks. TaskInsight provides high-level, quantitative information that can be correlated with tasks' performance variation over time to understand data reuse through the caches due to scheduling choices. TaskInsight is useful to diagnose and identify which scheduling decisions affected performance, when were they taken, and why the performance changed, both in single and multi-threaded executions. We demonstrate how TaskInsight can diagnose examples where poor scheduling caused over 10% difference in performance for tasks of the same type, due to changes in the tasks' data reuse through the private and shared caches, in single and multi-threaded executions of the same application. This flexible insight is key for optimization in many contexts, including data locality, throughput, memory footprint or even energy efficiency.We thank the reviewers for their feedback. This work was supported by the Swedish Research Council, the Swedish Foundation for Strategic Research project FFL12-0051 and carried out within the Linnaeus Centre of Excellence UPMARC, Uppsala Programming for Multicore Architectures Research Center. This paper was also published with the support of the HiPEAC network that received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 687698.Peer ReviewedPostprint (published version

Le problème de la composition parallèle : une approche supervisée

International audienceEnabling HPC applications to perform efficiently when invoking multiple parallel libraries simultaneously is a great challenge. Even if a single runtime system is used underneath, scheduling tasks or threads coming from different libraries over the same set of hardware resources introduce many issues, such as resource oversubscription, undesirable cache flushes or memory bus contention. This paper presents an extension of starpu, a runtime system specifically designed for heterogeneous architectures, that allows multiple parallel codes to run concurrently with minimal interference. Such parallel codes run within \emph{scheduling contexts} that provide confined execution environments which can be used to partition computing resources. Scheduling contexts can be dynamically resized to optimize the allocation of computing resources among concurrently running libraries. We introduce a \emph{hypervisor} that automatically expands or shrinks contexts using feedback from the runtime system (e.g. resource utilization). We demonstrate the relevance of our approach using benchmarks invoking multiple high performance linear algebra kernels simultaneously on top of heterogeneous multicore machines. We show that our mechanism can dramatically improve the overall application run time (-34%), most notably by reducing the average cache miss ratio (-50%).L'utilisation simultanée de plusieurs bibliothèques de calcul parallèle au sein d'une application soulève bien sou-vent des problèmes d'efficacité. En compétition pour l'obtention des ressources, les routines parallèles, pourtant optimisées, se gênent et l'on voit alors apparaître des phénomènes de surcharge, de contention ou de défaut de cache. Nous présentons une technique de cloisonnement de flux de calculs qui permet de limiter les effets de telles inter-férences. Le cloisonnement est réalisé à l'aide de contextes d'exécution qui partitionnement les unités de calculs voire en partagent certaines. La répartition des ressources entre les contextes peut être modifiée dynamiquement afin d'optimiser le rendement de la machine. À cette fin, nous proposons l'utilisation de métriques par un super- viseur pour redistribuer automatiquement les ressources aux contextes. Nous décrivons l'intégration des contextes d'exécution au support d'exécution pour machines hétérogènes StarPU et présentons des résultats d'expériences démontrant la pertinence de notre approche

Composabilité de codes parallèles sur architectures hétérogènes

Multicore machines equipped with accelerators are becoming increasingly popular in the High Performance Computing comunity. Due to the lack of consensus regarding the definition of a standard programming model for such machines, an increasing number of HPC developers are manually combining multiple programming environments to effectively use every underlying processing unit. In this document we present a framework which is able to dynamically alocate the computing ressources to the upper layers with great flexibility, in order to allow parallel applications to be seamlessly developed by composing existing parallel kernels

Resource aggregation for task-based Cholesky Factorization on top of modern architectures

This paper is submitted for review to the Parallel Computing special issue for HCW and HeteroPar 16 workshopsHybrid computing platforms are now commonplace, featuring a large number of CPU cores and accelerators. This trend makes balancing computations between these heterogeneous resources performance critical. In this paper we propose ag-gregating several CPU cores in order to execute larger parallel tasks and improve load balancing between CPUs and accelerators. Additionally, we present our approach to exploit internal parallelism within tasks, by combining two runtime system schedulers: a global runtime system to schedule the main task graph and a local one one to cope with internal task parallelism. We demonstrate the relevance of our approach in the context of the dense Cholesky factorization kernel implemented on top of the StarPU task-based runtime system. We present experimental results showing that our solution outperforms state of the art implementations on two architectures: a modern heterogeneous machine and the Intel Xeon Phi Knights Landing

Copernicus Ocean State Report, issue 6

The 6th issue of the Copernicus OSR incorporates a large range of topics for the blue, white and green ocean for all European regional seas, and the global ocean over 1993–2020 with a special focus on 2020

The IDENTIFY study: the investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer - a multicentre observational study

Objective To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation. Patients and Methods This was an international multicentre prospective observational study. We included patients aged ≥16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries. Results Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3–34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1–30.2), UTUC (n = 128) 1.14% (95% CI 0.77–1.52), renal cancer (n = 107) 1.05% (95% CI 0.80–1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32–2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03–1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90–4.15; P < 0.001), male sex 1.30 (95% CI 1.14–1.50; P < 0.001), and smoking 2.70 (95% CI 2.30–3.18; P < 0.001). Conclusions A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer

Composability of parallel codes on heterogeneous architectures

Pour répondre aux besoins de précision et d'efficacité des simulations scientifiques, la communauté du Calcul Haute Performance augmente progressivement les demandes en terme de parallélisme, rajoutant ainsi un besoin croissant de réutiliser les bibliothèques parallèles optimisées pour les architectures complexes.L'utilisation simultanée de plusieurs bibliothèques de calcul parallèle au sein d'une application soulève bien souvent des problèmes d 'efficacité. En compétition pour l'obtention des ressources, les routines parallèles, pourtant optimisées, se gênent et l'on voit alors apparaître des phénomènes de surcharge, de contention ou de défaut de cache.Dans cette thèse, nous présentons une technique de cloisonnement de flux de calculs qui permet de limiter les effets de telles interférences. Le cloisonnement est réalisé à l'aide de contextes d'exécution qui partitionnement les unités de calculs voire en partagent certaines. La répartition des ressources entre les contextes peut être modifiée dynamiquement afin d'optimiser le rendement de la machine. A cette fin, nous proposons l'utilisation de certaines métriques par un superviseur pour redistribuer automatiquement les ressources aux contextes. Nous décrivons l'intégration des contextes d'ordonnancement au support d'exécution pour machines hétérogènes StarPU et présentons des résultats d'expériences démontrant la pertinence de notre approche. Dans ce but, nous avons implémenté une extension du solveur direct creux qr mumps dans la quelle nous avons fait appel à ces mécanismes d'allocation de ressources. A travers les contextes d'ordonnancement nous décrivons une nouvelle méthode de décomposition du problème basée sur un algorithme de \proportional mapping". Le superviseur permet de réadapter dynamiquement et automatiquement l'allocation des ressources au parallèlisme irrégulier de l'application. L'utilisation des contextes d'ordonnancement et du superviseur a amélioré la localité et la performance globale du solveur.To face the ever demanding requirements in term of accuracy and speed of scientific simulations, the High Performance community is constantly increasing the demands in term of parallelism, adding thus tremendous value to parallel libraries strongly optimized for highly complex architectures.Enabling HPC applications to perform efficiently when invoking multiple parallel libraries simultaneously is a great challenge. Even if a uniform runtime system is used underneath, scheduling tasks or threads coming from dfferent libraries over the same set of hardware resources introduces many issues, such as resource oversubscription, undesirable cache ushes or memory bus contention.In this thesis, we present an extension of StarPU, a runtime system specifically designed for heterogeneous architectures, that allows multiple parallel codes to run concurrently with minimal interference. Such parallel codes run within scheduling contexts that provide confined executionenvironments which can be used to partition computing resources. Scheduling contexts can be dynamically resized to optimize the allocation of computing resources among concurrently running libraries. We introduced a hypervisor that automatically expands or shrinks contexts using feedback from the runtime system (e.g. resource utilization). We demonstrated the relevance of this approach by extending an existing generic sparse direct solver (qr mumps) to use these mechanisms and introduced a new decomposition method based on proportional mapping that is used to build the scheduling contexts. In order to cope with the very irregular behavior of the application, the hypervisor manages dynamically the allocation of resources. By means of the scheduling contexts and the hypervisor we improved the locality and thus the overall performance of the solver

La composition des codes parallèles sur plates-formes hétérogènes

To face the ever demanding requirements in term of accuracy and speed of scientific simulations, the High Performance community is constantly increasing the demands in term of parallelism, adding thus tremendous value to parallel libraries strongly optimized for highly complex architectures.Enabling HPC applications to perform efficiently when invoking multiple parallel libraries simultaneously is a great challenge. Even if a uniform runtime system is used underneath, scheduling tasks or threads coming from dfferent libraries over the same set of hardware resources introduces many issues, such as resource oversubscription, undesirable cache ushes or memory bus contention.In this thesis, we present an extension of StarPU, a runtime system specifically designed for heterogeneous architectures, that allows multiple parallel codes to run concurrently with minimal interference. Such parallel codes run within scheduling contexts that provide confined executionenvironments which can be used to partition computing resources. Scheduling contexts can be dynamically resized to optimize the allocation of computing resources among concurrently running libraries. We introduced a hypervisor that automatically expands or shrinks contexts using feedback from the runtime system (e.g. resource utilization). We demonstrated the relevance of this approach by extending an existing generic sparse direct solver (qr mumps) to use these mechanisms and introduced a new decomposition method based on proportional mapping that is used to build the scheduling contexts. In order to cope with the very irregular behavior of the application, the hypervisor manages dynamically the allocation of resources. By means of the scheduling contexts and the hypervisor we improved the locality and thus the overall performance of the solver.Pour répondre aux besoins de précision et d'efficacité des simulations scientifiques, la communauté du Calcul Haute Performance augmente progressivement les demandes en terme de parallélisme, rajoutant ainsi un besoin croissant de réutiliser les bibliothèques parallèles optimisées pour les architectures complexes.L'utilisation simultanée de plusieurs bibliothèques de calcul parallèle au sein d'une application soulève bien souvent des problèmes d 'efficacité. En compétition pour l'obtention des ressources, les routines parallèles, pourtant optimisées, se gênent et l'on voit alors apparaître des phénomènes de surcharge, de contention ou de défaut de cache.Dans cette thèse, nous présentons une technique de cloisonnement de flux de calculs qui permet de limiter les effets de telles interférences. Le cloisonnement est réalisé à l'aide de contextes d'exécution qui partitionnement les unités de calculs voire en partagent certaines. La répartition des ressources entre les contextes peut être modifiée dynamiquement afin d'optimiser le rendement de la machine. A cette fin, nous proposons l'utilisation de certaines métriques par un superviseur pour redistribuer automatiquement les ressources aux contextes. Nous décrivons l'intégration des contextes d'ordonnancement au support d'exécution pour machines hétérogènes StarPU et présentons des résultats d'expériences démontrant la pertinence de notre approche. Dans ce but, nous avons implémenté une extension du solveur direct creux qr mumps dans la quelle nous avons fait appel à ces mécanismes d'allocation de ressources. A travers les contextes d'ordonnancement nous décrivons une nouvelle méthode de décomposition du problème basée sur un algorithme de \proportional mapping". Le superviseur permet de réadapter dynamiquement et automatiquement l'allocation des ressources au parallèlisme irrégulier de l'application. L'utilisation des contextes d'ordonnancement et du superviseur a amélioré la localité et la performance globale du solveur

Characterizing Task Scheduling Performance Based on Data Reuse

Resource Sharing ModelingUPMAR