| 1. |
- Boström, Magnus, 1972-, et al.
(författare)
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Conditions for Transformative Learning for Sustainable Development : A Theoretical Review and Approach
- 2018
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 10:12
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Tidskriftsartikel (refereegranskat)abstract
- Continued unsustainability and surpassed planetary boundaries require not only scientific and technological advances, but deep and enduring social and cultural changes. The purpose of this article is to contribute a theoretical approach to understand conditions and constraints for societal change towards sustainable development. In order to break with unsustainable norms, habits, practices, and structures, there is a need for learning for transformation, not only adaption. Based on a critical literature review within the field of learning for sustainable development, our approach is a development of the concept of transformative learning, by integrating three additional dimensions—Institutional Structures, Social Practices, and Conflict Perspectives. This approach acknowledges conflicts on macro, meso, and micro levels, as well as structural and cultural constraints. It contends that transformative learning is processual, interactional, long-term, and cumbersome. It takes place within existing institutions and social practices, while also transcending them. The article adopts an interdisciplinary social science perspective that acknowledges the importance of transformative learning in order for communities, organizations, and individuals to be able to deal with global sustainability problems, acknowledging the societal and personal conflicts involved in such transformation.
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| 2. |
- Berg, Erik, et al.
(författare)
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A Statistical Multiprocessor Cache Model
- 2006
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Ingår i: Proc. International Symposium on Performance Analysis of Systems and Software. - Piscataway, NJ : IEEE. - 1424401860 ; , s. 89-99, s. 89-99
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Konferensbidrag (refereegranskat)
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| 3. |
- Berg, Erik, et al.
(författare)
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A Statistical Multiprocessor Cache Model
- 2005
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- The introduction of general purpose microprocessors running multiple threads will put a focus on methods and tools helping a programmer to write efficient parallel applications. Such tool should be fast enough to meet a software developer's need for short turn-around time, but also accurate and flexible enough to provide trend-correct and intuitive feedback. This paper describes an efficient and flexible approach for modeling the memory system of a multiprocessor, such as those of chip multiprocessors (CMPs). Sparse data is sampled during a multithreaded execution. The data collected consist of the reuse distance and invalidation distribution for a small subset of the memory accesses. Based on the sampled data from a single run, a new mathematical formula can be used to estimate the miss rate for a memory hierarchy built from caches of arbitrarily size, cacheline size and degree of sharing. The formula further divides the misses into six categories to further aid the software developer. The method is evaluated using a large number of commercial and technical multithreaded applications. The result produced by our algorithm fed with sparse sampling data is shown to be consistent with results gathered during traditional architecture simulation.
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| 4. |
- Berg, Erik, 1970-
(författare)
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Efficient and Flexible Characterization of Data Locality through Native Execution Sampling
- 2005
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Data locality is central to modern computer designs. The widening gap between processor speed and memory latency has introduced the need for a deep hierarchy of caches. Thus, the performance of an application is to a large extent dependent on the amount of data locality the caches can exploit. Some data locality comes naturally from the way most programs are written and the way their data is allocated in the memory. Compilers further try to create data locality by loop transformations and optimized data layout. Different ways of writing a program and/or laying out its data may improve an application’s locality even more. However, it is far from obvious how such a locality optimization can be achieved, especially since the optimizing compiler may have left the optimization job half done. Thus, efficient tools are needed to guide the software developers on their quest for data locality. The main contribution of this dissertation is a sample-based novel method for analyzing the data locality of an application. Very sparse data is collected during a single execution of the studied application. The sparse sampling adds a minimum overhead to the execution time, which enables complex applications running realistic data sets to be studied. The architecturalindependent information collected during the execution is fed to a mathematical cache model for predicting the cache miss ratio. The sparsely-collected data can be used to characterize the application’s data locality in respect to almost any possible cache hierarchy, such as complicated multiprocessor memory systems with multilevel cache hierarchies. Any combination of cache size, cache line size and degree of sharing can be modeled. Each new modeled design point takes only a fraction of a second to evaluate, even though the application from which the sampled data was collected may have executed for hours. This makes the tool not just usable for software developers, but also for hardware developers who need to evaluate a huge memory-system design space. We also discuss different ways of presenting data-locality information to a programmer in an intuitive and easily interpreted way. Some of the locality metrics we introduce utilize the flexibility of our algorithm and its ability to vary different cache parameters for one run. The dissertation also presents several prototype implementations of tools for profiling the memory system.
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| 5. |
- Berg, Erik, et al.
(författare)
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Efficient Data-Locality Analysis of Long-Running Applications
- 2004
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Analysis of application data cache behavior is important for program optimization and architectural design decisions. Current methods include hardware monitoring and simulation, but these methods lack from either limited flexibility or large run-time overhead that prevents realistic workloads. This paper describes a new fast and flexible tool based on StatCache. This tool is based on a probabilistic cache model instead of a functional cache simulator and use sparsely sampled run-time information instead of complete traces or sampled contiguous subtraces. A post-run analyzer calculates miss ratios of fully associative caches of arbitrary size and cache line size, from statistics gathered at a single run. It can also produce various data-locality metrics and give data-structure centric data-locality figures. The implementation utilizes simple-hardware and operating-system support available in most operating systems and runs uninstrumented optimized code. We evaluate the method using the SPEC benchmark suite using the largest (ref) input sets and show that the accuracy is high. We also show the run-time overhead for this flexible “cache simulator” to be less than 20% for long-running applications, much faster than current simulators.
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| 6. |
- Berg, Erik, et al.
(författare)
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Fast Data-Locality Profiling of Native Execution
- 2005
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Ingår i: ACM SIGMETRICS Performance Evaluation Review. - : Association for Computing Machinery (ACM). - 0163-5999. ; 33:1, s. 169-180
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Tidskriftsartikel (refereegranskat)abstract
- Performance tools based on hardware counters can efficiently profile the cache behavior of an application and help software developers improve its cache utilization. Simulator-based tools can potentially provide more insights and flexibility and model many different cache configurations, but have the drawback of large run-time overhead.We present StatCache, a performance tool based on a statistical cache model. It has a small run-time overhead while providing much of the flexibility of simulator-based tools. A monitor process running in the background collects sparse memory access statistics about the analyzed application running natively on a host computer. Generic locality information is derived and presented in a code-centric and/or data-centric view.We evaluate the accuracy and performance of the tool using ten SPEC CPU2000 benchmarks. We also exemplify how the flexibility of the tool can be used to better understand the characteristics of cache-related performance problems.
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| 7. |
- Berg, Erik, et al.
(författare)
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Low-Overhead Spatial and Temporal Data Locality Analysis
- 2003
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- Performance is getting increasingly sensitive to cache behavior because of the growing gap between processor cycle time and memory latency. To improve performance, applications need to be optimized for data locality. Run-time analysis of spatial and temporal data locality can be used to facilitate this and should help both manual tuning and feedback-based compiler optimizations. Identifying cache behavior of individual data structures further enhances the optimization process. Current methods to perform such analysis include simulation combined with set sampling or time sampling, and hardware monitoring. Sampling often suffers from either poor accuracy or large run-time overhead, while hardware measurements have limited flexibility.We present DLTune, a prototype tool that performs spatial and temporal data-locality analysis in run time. It measures both spatial and temporal locality for the entire application and individual data structures in a single run, and effectively exposes poor data locality based on miss ratio estimates of fully-associative caches. The tool is based on an elaborate and novel sampling technique that allows all information to be collected in a single run with an overall sampling rate as low as one memory reference in ten million and an average slowdown below five on large workloads.
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| 8. |
- Berg, Erik
(författare)
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Methods for run time analysis of data locality
- 2003
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The growing gap between processor clock speed and DRAM access time puts new demands on software and development tools. Deep memory hierarchies and high cache miss penalties in present and emerging computer systems make execution time sensitive to data locality. Therefore, developers of performance-critical applications and optimizing compilers must be aware of data locality and maximize cache utilization to produce fast code. To aid the optimization process and help understanding data locality, we need methods to analyze programs and pinpoint poor cache utilization and possible optimization opportunities.Current methods for run-time analysis of data locality and cache behavior include functional cache simulation, often combined with set sampling or time sampling, other regularity metrics based on strides and data streams, and hardware monitoring. However, they all share the trade-off between run-time overhead, accuracy and explanatory power.This thesis presents methods to efficiently analyze data locality at run time based on cache modeling. It suggests source-interdependence profiling as a technique for examining the cache behavior of applications and locating source code statements and/or data structures that cause poor cache utilization. The thesis also introduces a novel statistical cache-modeling technique, StatCache. Rather than implementing a functional cache simulator, StatCache estimates the miss ratios of fully-associative caches using probability theory. A major advantage of the method is that the miss ratio estimates can be based on very sparse sampling. Further, a single run of an application is enough to estimate the miss ratio of caches of arbitrary sizes and line sizes and to study both spatial and temporal data locality.
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