ACM Queue - Performance

ACM Queue - Performance http://queue.acm.org/listing.cfm?item_topic=Performance&qc_type=topics_list&filter=Performance&page_title=Performance&order=desc You Don't know Jack about Application Performance: Knowing whether you're doomed to fail is important when starting a project. http://queue.acm.org/detail.cfm?id=3595862 Performance 2023年5月24日 12:51:51 GMT David Collier-Brown 3595862 All Sliders to the Right: Hardware Overkill http://queue.acm.org/detail.cfm?id=3580505 Performance 2023年2月13日 14:54:20 GMT George V. Neville-Neil 3580505 Reinventing Backend Subsetting at Google: Designing an algorithm with reduced connection churn that could replace deterministic subsetting http://queue.acm.org/detail.cfm?id=3570937 Performance 2022年12月14日 11:47:30 GMT Peter Ward, Paul Wankadia, Kavita Guliani 3570937 The Time I Stole 10,000ドル from Bell Labs: Or why DevOps encourages us to celebrate outages http://queue.acm.org/detail.cfm?id=3434773 Performance 2020年11月11日 13:00:27 GMT Thomas A. Limoncelli 3434773 FPGAs in Data Centers: FPGAs are slowly leaving the niche space they have occupied for decades. http://queue.acm.org/detail.cfm?id=3231573 This installment of Research for Practice features a curated selection from Gustavo Alonso, who provides an overview of recent developments utilizing FPGAs (field-programmable gate arrays) in datacenters. As Moore's Law has slowed and the computational overheads of datacenter workloads such as model serving and data processing have continued to rise, FPGAs offer an increasingly attractive point in the trade-off between power and performance. Gustavo's selections highlight early successes and practical deployment considerations that inform the ongoing, high-stakes debate about the future of datacenter- and cloud-based computation substrates. Performance 2018年6月05日 14:13:40 GMT Gustavo Alonso 3231573 Workload Frequency Scaling Law - Derivation and Verification: Workload scalability has a cascade relation via the scale factor. http://queue.acm.org/detail.cfm?id=3229201 This article presents equations that relate to workload utilization scaling at a per-DVFS subsystem level. A relation between frequency, utilization, and scale factor (which itself varies with frequency) is established. The verification of these equations turns out to be tricky, since inherent to workload, the utilization also varies seemingly in an unspecified manner at the granularity of governance samples. Thus, a novel approach called histogram ridge trace is applied. Quantifying the scaling impact is critical when treating DVFS as a building block. Typical application includes DVFS governors and or other layers that influence utilization, power, and performance of the system. The scope here though, is limited to demonstrating well-quantified and verified scaling equations. Performance 2018年5月24日 17:48:56 GMT Noor Mubeen 3229201 Monitoring in a DevOps World: Perfect should never be the enemy of better. http://queue.acm.org/detail.cfm?id=3178371 Monitoring can seem quite overwhelming. The most important thing to remember is that perfect should never be the enemy of better. DevOps enables highly iterative improvement within organizations. If you have no monitoring, get something; get anything. Something is better than nothing, and if you've embraced DevOps, you've already signed up for making it better over time. Performance 2018年1月08日 16:05:18 GMT Theo Schlossnagle 3178371 Idle-Time Garbage-Collection Scheduling: Taking advantage of idleness to reduce dropped frames and memory consumption http://queue.acm.org/detail.cfm?id=2977741 Google's Chrome web browser strives to deliver a smooth user experience. An animation will update the screen at 60 FPS (frames per second), giving Chrome around 16.6 milliseconds to perform the update. Within these 16.6 ms, all input events have to be processed, all animations have to be performed, and finally the frame has to be rendered. A missed deadline will result in dropped frames. These are visible to the user and degrade the user experience. Such sporadic animation artifacts are referred to here as jank. This article describes an approach implemented in the JavaScript engine V8, used by Chrome, to schedule garbage-collection pauses during times when Chrome is idle. This approach can reduce user-visible jank on real-world web pages and results in fewer dropped frames. Performance 2016年7月26日 16:37:27 GMT Ulan Degenbaev, Jochen Eisinger, Manfred Ernst, Ross McIlroy, Hannes Payer 2977741 Hadoop Superlinear Scalability: The perpetual motion of parallel performance http://queue.acm.org/detail.cfm?id=2789974 "We often see more than 100 percent speedup efficiency!" came the rejoinder to the innocent reminder that you can't have more than 100 percent of anything. But this was just the first volley from software engineers during a presentation on how to quantify computer system scalability in terms of the speedup metric. In different venues, on subsequent occasions, that retort seemed to grow into a veritable chorus that not only was superlinear speedup commonly observed, but also the model used to quantify scalability for the past 20 years failed when applied to superlinear speedup data. Performance 2015年6月04日 20:08:33 GMT Neil Gunther, Paul Puglia, Kristofer Tomasette 2789974 The API Performance Contract: How can the expected interactions between caller and implementation be guaranteed? http://queue.acm.org/detail.cfm?id=2576968 When you call functions in an API, you expect them to work correctly; sometimes this expectation is called a contract between the caller and the implementation. Callers also have performance expectations about these functions, and often the success of a software system depends on the API meeting these expectations. So there's a performance contract as well as a correctness contract. The performance contract is usually implicit, often vague, and sometimes breached (by caller or implementation). How can this aspect of API design and documentation be improved? Performance 2014年1月30日 15:37:49 GMT Robert Sproull, Jim Waldo 2576968