Karl Taht's Research Paper Blog

Authors: Grant Ayers et al. Venue: ISCA 2019 Previous works have highlighted a significant frontend bottleneck is warehouse-scale computers (WSC). A variety of solutions have been proposed, both on real hardware and architectural papers, to mitigate the issue. This paper performs deep analysis across 90%+ of Google's entire fleet to perform a fine-grain analysis of when, where, and how frontend bottlenecks occur. AsmDB comprises of post-processed last branch record (LBR) data to form control flow probabilities and precise information about what instructions triggered I-cache misses. The analysis shows several core reasons for I-cache misses: large jump distances (either via function call or indirect branch) and cold code being brought into the cache (either via cache blocks or prefetching). The remainder of the paper transitions to being much more compiler focused. It describes a software prefetching algorithm which utilizes AsmDB information to inject prefetches at ideal poin...

Authors: Akshitha Sriraman, Abhishek Dhanotia, Thomas F. Wenisch Venue: ISCA 2019 This work comprises of two main parts: a detailed analysis and tool to improve course-grain parameters based on general application (microservice) behavior. The author's analyze workloads in Facebook's datacenter in the categories of Web, Feed, Ads, and Cache which have varying throughput and latency requirements. The data center workloads exhibit significant front-end stalls (instruction fetch misses), significant branch  mispredictions, and significant back-end stalls (mostly data cache misses). uSKU is presented as a tool which automates the process of parameter tuning in an effort to improve system optimization for specific classes of microservices. Core frequency, uncore frequency, core count, code-and-data prioritization, prefetchers, transparent and static huge pages are explored. Knobs are tested independently and thus to do not consider dependent effects (Gaussian process search seem...

Authors: Hongzi Mao, Malte Schwarzkopf, Shaileshh Bojja Venkatakrishnan, Zili Meng, Mohammad Alizadeh Venue: Proceedings of the ACM Special Interest Group on Data Communication This paper utilizes reinforcement learning to schedule learn a scheduling policy for Spark jobs. The scheduler has two main decisions: (i) what stage to schedule and (ii) how much parallelism to exploit for that stage. The RL problem is formulated as given the state of the cluster and DAG input, output a scheduling action. Reward is defined as -T x J where T is the time step and J is the number of jobs in the system. The decision making process is particularly difficult because an job can present a DAG of any shape for dependencies, yet, the neural network input is of fixed size. To solve this, a method based on graph convolutional neural networks [1] is used. The RL policy network predicts a composite action of stage of maximum parallelism level. To train the network in the case of continuous job arrivals, ...

Authors: Kostis Kaffes, Timothy Chong, Jack Tigar Humphries, Adam Belay, David Mazieres, Christos Kozyrakis Venue: NSDI 2019 Shinjuku is a dataplane operating system that leverages hardware support for virtualization to enable microsecond-scale preemption. In network processing, there is a fundamental challenge between optimizing for throughput and latency. Interrupt cores too frequently, and throughput will drop because of context switching overheads. Conversely, infrequent preemption can lead to poor tail latency, as short requests can get stuck behind long requests. This is particularly prevalent in bimodal distributions -- such as DB server processing short get() and put() requests while also servicing scans. Shinjuku first greatly reduces context switching overhead by leveraging Dune, enabling direct access to APICs, and other optimizations. With low-overhead context swap enabled, the authors then focus on an effective preemptive scheduling algorithms. The authors utilize cen...

Authors: Ravi Iyer, Ramesh Illikkal, Omesh Tickoo, Li Zhao, Padma Apparo, Don Newell Venue:   Computer Networks 2009 The authors of this paper seek to understand the importance of resource allocation in a VM/cloud environment. At the time of publishing, only time-multiplexing and core allocation isolated VMs from a performance standpoint, which they refer to as a Virtual Platform Architecture (VPA) . The authors suggest that cache space, memory, bandwidth and power equally need to be virtualized as well. They focus on memory bandwidth and cache allocation. They motivate the problem by performing measurements and effects of resource contention and show significant performance degradation. Then then show that a simplistic model can perform fairly accurate predictions of cache occupancy, MPI, and cache contention. Perhaps the most elegant part of the paper is the description of the cache and memory bandwidth monitoring and allocation technology, which I assume laid foundatio...