| 1. |
- Kessler, Martin, et al.
(författare)
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Exploiting Instruction Level Parallelism for REPLICA - A Configurable VLIW Architecture With Chained Functional Units
- 2012
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In this paper we present a scheduling algorithm for VLIW architectures with chained functional units. We show how our algorithm can help speed up programs at the instruction level, for an architecture called REPLICA, a configurable emulated shared memory (CESM) architecture whose computation model is based on the PRAM model. Since our LLVM based compiler is parameterizable in the number of different functional units, read and write ports to register file etc. we can generate code for different REPLICA architectures that have different functional unit configurations. We show for a set of different configurations how our implementation can produce high quality code; and we argue that the high parametrization of the compiler makes it, together with the simulator, useful for hardware/software co-design.
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| 2. |
- Schuette, Moritz, et al.
(författare)
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Molecular dissection of colorectal cancer in pre-clinical models identifies biomarkers predicting sensitivity to EGFR inhibitors
- 2017
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Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 8
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Tidskriftsartikel (refereegranskat)abstract
- Colorectal carcinoma represents a heterogeneous entity, with only a fraction of the tumours responding to available therapies, requiring a better molecular understanding of the disease in precision oncology. To address this challenge, the OncoTrack consortium recruited 106 CRC patients (stages I-IV) and developed a pre-clinical platform generating a compendium of drug sensitivity data totalling 44,000 assays testing 16 clinical drugs on patient-derived in vivo and in vitro models. This large biobank of 106 tumours, 35 organoids and 59 xenografts, with extensive omics data comparing donor tumours and derived models provides a resource for advancing our understanding of CRC. Models recapitulate many of the genetic and transcriptomic features of the donors, but defined less complex molecular sub-groups because of the loss of human stroma. Linking molecular profiles with drug sensitivity patterns identifies novel biomarkers, including a signature outperforming RAS/RAF mutations in predicting sensitivity to the EGFR inhibitor cetuximab.
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| 3. |
- Ali, Akhtar, et al.
(författare)
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OpenCL for programming shared memory multicore CPUs
- 2011
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Ingår i: Fourth Swedish Workshop on Multi-Core Computing MCC-2011. - Linköping : Linköping University. ; , s. 65-70
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In this work, we evaluate the effectiveness of OpenCL for programming multicore CPUs in a comparative case study with OpenMP and Intel TBB for five benchmark applications: matrix multiply, LU decomposition, 2D image convolution, Pi value approximation and image histogram generation.
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| 4. |
- Ali, Akhtar, et al.
(författare)
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OpenCL for programming shared memory multicore CPUs
- 2012
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Ingår i: Proceedings of the 5th Workshop on MULTIPROG2012. - : HiPEAC Network of Excellence.
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Konferensbidrag (refereegranskat)abstract
- Shared memory multicore processor technology is pervasive in mainstream computing. This new architecture challenges programmers to write code that scales over these many cores to exploit the full computational power of these machines. OpenMP and Intel Threading Building Blocks (TBB) are two of the popular frameworks used to program these architectures. Recently, OpenCL has been defined as a standard by Khronos group which focuses on programming a possibly heterogeneous set of processors with many cores such as CPU cores, GPUs, DSP processors. In this work, we evaluate the effectiveness of OpenCL for programming multicore CPUs in a comparative case study with OpenMP and Intel TBB for five benchmark applications: matrix multiply, LU decomposition,2D image convolution, Pi value approximation and image histogram generation. The evaluation includes the effect of compiler optimizations for different frameworks, OpenCL performance on different vendors’ platformsand the performance gap between CPU-specific and GPU-specific OpenCL algorithms for execution on a modern GPU. Furthermore, a brief usability evaluation of the three frameworks is also presented.
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| 5. |
- Amaral, Vasco, et al.
(författare)
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Programming Languages for Data-Intensive HPC Applications : a Systematic Mapping Study
- 2020
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Ingår i: Parallel Computing. - : Elsevier. - 0167-8191 .- 1872-7336. ; 91, s. 1-17
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Tidskriftsartikel (refereegranskat)abstract
- A major challenge in modelling and simulation is the need to combine expertise in both software technologies and a given scientific domain. When High-Performance Computing (HPC) is required to solve a scientific problem, software development becomes a problematic issue. Considering the complexity of the software for HPC, it is useful to identify programming languages that can be used to alleviate this issue. Because the existing literature on the topic of HPC is very dispersed, we performed a Systematic Mapping Study (SMS) in the context of the European COST Action cHiPSet. This literature study maps characteristics of various programming languages for data-intensive HPC applications, including category, typical user profiles, effectiveness, and type of articles. We organised the SMS in two phases. In the first phase, relevant articles are identified employing an automated keyword-based search in eight digital libraries. This lead to an initial sample of 420 papers, which was then narrowed down in a second phase by human inspection of article abstracts, titles and keywords to 152 relevant articles published in the period 2006–2018. The analysis of these articles enabled us to identify 26 programming languages referred to in 33 of relevant articles. We compared the outcome of the mapping study with results of our questionnaire-based survey that involved 57 HPC experts. The mapping study and the survey revealed that the desired features of programming languages for data-intensive HPC applications are portability, performance and usability. Furthermore, we observed that the majority of the programming languages used in the context of data-intensive HPC applications are text-based general-purpose programming languages. Typically these have a steep learning curve, which makes them difficult to adopt. We believe that the outcome of this study will inspire future research and development in programming languages for data-intensive HPC applications.
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| 6. |
- Andersson, Jesper, et al.
(författare)
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Profile-Guided Composition
- 2008
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Ingår i: 7th Int. Symposium on Software Composition SC 2008,2008. - Berlin : Springer. ; , s. 157-
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Konferensbidrag (refereegranskat)abstract
- We present an approach that generates context-aware, optimized libraries of algorithms and data structures. The search space contains all combinations of implementation variants of algorithms and data structures including dynamically switching and converting between them. Based on profiling, the best implementation for a certain context is precomputed at deployment time and selected at runtime. In our experiments, the profile-guided composition outperforms the individual variants in almost all cases.
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| 7. |
- Aronsson, Peter, 1974-
(författare)
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Automatic Parallelization of Equation-Based Simulation Programs
- 2006
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Modern equation-based object-oriented modeling languages which have emerged during the past decades make it easier to build models of large and complex systems. The increasing size and complexity of modeled systems requires high performance execution of the simulation code derived from such models. More efficient compilation and code optimization techniques can help to some extent. However, a number of heavy-duty simulation applications require the use of high performance parallel computers in order to obtain acceptable execution times. Unfortunately, the possible additional performance offered by parallel computer architectures requires the simulation program to be expressed in a way that makes the potential parallelism accessible to the parallel computer. Manual parallelization of computer programs is generally a tedious and error prone process. Therefore, it would be very attractive to achieve automatic parallelization of simulation programs.This thesis presents solutions to the research problem of finding practically usable methods for automatic parallelization of simulation codes produced from models in typical equationbased object-oriented languages. The methods have been implemented in a tool to automatically translate models in the Modelica modeling language to parallel codes which can be efficiently executed on parallel computers. The tool has been evaluated on several application models. The research problem includes the problem of how to extract a sufficient amount of parallelism from equations represented in the form of a data dependency graph (task graph), requiring analysis of the code at a level as detailed as individual expressions. Moreover, efficient clustering algorithms for building clusters of tasks from the task graph are also required. One of the major contributions of this thesis work is a new approach for merging fine-grained tasks by using a graph rewrite system. Results from using this method show that it is efficient in merging task graphs, thereby decreasing their size, while still retaining a reasonable amount of parallelism. Moreover, the new task-merging approach is generally applicable to programs which can be represented as static (or almost static) task graphs, not only to code from equation-based models.An early prototype called DSBPart was developed to perform parallelization of codes produced by the Dymola tool. The final research prototype is the ModPar tool which is part of the OpenModelica framework. Results from using the DSBpart and ModPar tools show that the amount of parallelism of complex models varies substantially between different application models, and in some cases can produce reasonable speedups. Also, different optimization techniques used on the system of equations from a model affect the amount of parallelism of the model and thus influence how much is gained by parallelization.
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| 8. |
- Avdic, Kenan, et al.
(författare)
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Pipelined parallel sorting on the Intel SCC
- 2011
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Ingår i: Fourth Swedish Workshop on Multi-Core Computing MCC-2011. - Linköping : Linköping University. ; , s. 96-101
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The Single-Chip Cloud Computer (SCC) is an experimental processor created by Intel Labs. It comprises 48 Intel-IA32 cores linked by an on-chip high performance mesh network, as well as four DDR3 memory controllers to access an off-chip main memory. We investigate the adaptation of sorting onto SCC as an algorithm engineering problem. We argue that a combination of pipelined mergesort and sample sort will fit best to SCC's architecture. We also provide a mapping based on integer linear programming to address load balancing and latency considerations. We describe a prototype implementation of our proposai together with preliminary runtime measurements, that indicate the usefulness of this approach. As mergesort can be considered as a representative of the class of streaming applications, the techniques deveioped here should also apply to the other problems in this class, such as many applications for parallel embedded systems, i.e. MPSoC.
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| 9. |
- Bednarski, Andrzej, 1975-, et al.
(författare)
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Energy-Optimal Integrated VLIW Code Generation
- 2004
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Ingår i: CPC04 11th Int. Workshop on Compilers for Parallel Computers,2004. ; , s. 227-238
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)
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| 10. |
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