Showing posts with label wasm. Show all posts
Showing posts with label wasm. Show all posts

Tuesday, June 09, 2020

[Links of the Day] 09/06/2020 : #WASM on #K8S , Fast Anomaly Detection on Graphs, Linux one ring

  • Krustlet : it seems that web assembly is getting more pervasive, we have kernel WASM, WASM for Deep learning, now Krustlet offer WASM for Kubernetes via Kubelet.
  • Fast Anomaly Detection in Graphs :  Really cool real-time anomaly detection on dynamic graphs.  the authors claim to be 644 times faster than SOTA with 42-48% higher accuracy. What is even more attractive is the constant memory usage which is fantastic for production deployment. [github] 
  • io_uring : this will dominate the future of the Linux interface. It is currently eating up every single IO interface and probably won't stop just there. 


Thursday, May 14, 2020

[Links of the Day] 14/05/2020 : Wasm In Linux Kernel, Knowledge Graphs, Contrastive Machine learning Model for Software Performance Regressions

  • Kernel Wasm : Looks like people want to run WASM everywhere. This time the authors propose to run wasm program in the kernel. In this case, I just wonder if it would not be more judicious to try run WASM in EBPF. From the GitHub repo it seems that they might actually try to do the opposite. [github]
  • Knowledge Graphs : A comprehensive introduction to knowledge graphs. If you want to learn more about the knowledge graph I would recommend reading the following paper before reading the arxiv one.
  • A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions : really innovative approach by Intel folks there. I have started to see interesting trends in machine learning where instead of trying to train the ML model using a dataset that contains the whole spectrum of possibility. The authors start to use contrastive methods instead. In this case, the ML model is trained on a non-abnormal dataset in order to identify abnormal behaviour. It is much easier in performance evaluation to obtain ideal, or standard metrics rather than abnormal scenario. In this case, the author uses the ideal hardware performance counter to train their model in order to identify abnormal behaviour. [poster]


Thursday, February 27, 2020

[Links of the Day] 27/02/2020 : Finance Datasets, OS build using WASM and IPFS, Team Wiki

  • Quandl : financial, economic, and alternative datasets for financial professionals. A treasure trove of information, sadly not all free.
  • RedShirt : Operating system build using WASM that you can run off straight from IPFS ... Next thing you know we will have a blockchain-based package management system for an end to end trustworthy OS... Well as long as you have access to the internet.
  • Outline : wiki and knowledgebase for growing teams using the markdown language. You can run it straight of docker.