| 1. |
- Jalby, William, et al.
(författare)
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The Long and Winding Road Toward Efficient High-Performance Computing
- 2018
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Ingår i: Proceedings of the IEEE. - 0018-9219 .- 1558-2256. ; 106:11, s. 1985-2003
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Tidskriftsartikel (refereegranskat)abstract
- The major challenge to Exaflop computing, and more generally, efficient high-end computing, is in finding the best "matches" between advanced hardware capabilities and the software used to program applications, so that top performance will be achieved. Several benchmarks show very disappointing performance progress over the last decade, clearly indicating a mismatch between hardware and software. To remedy this problem, it is important that key performance enablers at the software level-autotuning, performance analysis tools, full application optimization-are understood. For each area, we highlight major limitations and most promising approaches to reaching better performance and energy levels. Finally, we conclude by analyzing hardware and software design, trying to pave the way for more tightly integrated hardware and software codesign.
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| 2. |
- Popov, Mihail, et al.
(författare)
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Efficient thread/page/parallelism autotuning for NUMA systems
- 2019
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Ingår i: ICS '19. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450360791 ; , s. 342-353
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Konferensbidrag (refereegranskat)abstract
- Current multi-socket systems have complex memory hierarchies with significant Non-Uniform Memory Access (NUMA) effects: memory performance depends on the location of the data and the thread. This complexity means that thread- and data-mappings have a significant impact on performance. However, it is hard to find efficient data mappings and thread configurations due to the complex interactions between applications and systems.In this paper we explore the combined search space of thread mappings, data mappings, number of NUMA nodes, and degreeof-parallelism, per application phase, and across multiple systems. We show that there are significant performance benefits from optimizing this wide range of parameters together. However, such an optimization presents two challenges: accurately modeling the performance impact of configurations across applications and systems, and exploring the vast space of configurations. To overcome the modeling challenge, we use native execution of small, representative codelets, which reproduce the system and application interactions. To make the search practical, we build a search space by combining a range of state of the art thread- and data-mapping policies.Combining these two approaches results in a tractable search space that can be quickly and accurately evaluated without sacrificing significant performance. This search finds non-intuitive configurations that perform significantly better than previous works. With this approach we are able to achieve an average speedup of 1.97× on a four node NUMA system
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| 3. |
- Sánchez Barrera, Isaac, et al.
(författare)
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Modeling and Optimizing NUMA Effects and Prefetching with Machine Learning
- 2020
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Ingår i: ICS '20: Proceedings of the 34th ACM International Conference on Supercomputing. - New York, NY, USA : ACM.
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Konferensbidrag (refereegranskat)abstract
- Both NUMA thread/data placement and hardware prefetcher configuration have significant impacts on HPC performance. Optimizing both together leads to a large and complex design space that has previously been impractical to explore at runtime.In this work we deliver the performance benefits of optimizing both NUMA thread/data placement and prefetcher configuration at runtime through careful modeling and online profiling. To address the large design space, we propose a prediction model that reduces the amount of input information needed and the complexity of the prediction required. We do so by selecting a subset of performance counters and application configurations that provide the richest profile information as inputs, and by limiting the output predictions to a subset of configurations that cover most of the performance.Our model is robust and can choose near-optimal NUMA+Prefetcher configurations for applications from only two profile runs. We further demonstrate how to profile online with low overhead, resulting in a technique that delivers an average of 1.68× performance improvement over a locality-optimized NUMA baseline with all prefetchers enabled.
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| 4. |
- Shimchenko, Marina, et al.
(författare)
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Analysing and Predicting Energy Consumption of Garbage Collectors in OpenJDK
- 2022
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Ingår i: MPLR '22. - New York : Association for Computing Machinery (ACM). - 9781450396967 ; , s. 3-15
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Konferensbidrag (refereegranskat)abstract
- Sustainable computing needs energy-efficient software. This paper explores the potential of leveraging the nature of software written in managed languages: increasing energy efficiency by changing a program’s memory management strategy without altering a single line of code. To this end, we perform comprehensive energy profiling of 35 Java applications across four benchmarks. In many cases, we find that it is possible to save energy by replacing the default G1 collector with another without sacrificing performance. Furthermore, potential energy savings can be even higher if performance regressions are permitted. Inspired by these results, we study what the most energy-efficient GCs are to help developers prune the search space for energy profiling at a low cost. Finally, we show that machine learning can be successfully applied to the problem of finding an energy-efficient GC configuration for an application, reducing the cost even further.
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