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Meltdown and Spectre bugs: Difference between revisions
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Meltdown and Spectre are bugs related to speculative execution in a variety of CPU architectures developed during the past ten to fifteen years and which affect in particular processors from Intel and AMD, including those in use on Compute Canada clusters. A detailed discussion of the two bugs can be found on [https://arstechnica.com/gadgets/2018/01/meltdown-and-spectre-every-modern-processor-has-unfixable-security-flaws/ this page] and Compute Canada personnel are currently patching all of the systems vulnerable to attacks based on these bugs. What sort of performance degradation users will observe as a consequence of the patches is dependent on the software you are using and how it interacts with the operating system but in general the more filesystem activity and other input/output operations that a program performs during its execution, the more likely it is to suffer from a slowdown. Some benchmarks of the performance loss for AI and machine learning codes are publicly [https://medium.com/implodinggradients/meltdown-c24a9d5e254e available] and we recommend that users consider running some simple tests of their own to see if there is any substantial loss of performance with their own code(s). | Meltdown and Spectre are bugs related to speculative execution in a variety of CPU architectures developed during the past ten to fifteen years and which affect in particular processors from Intel and AMD, including those in use on Compute Canada clusters. A detailed discussion of the two bugs can be found on [https://arstechnica.com/gadgets/2018/01/meltdown-and-spectre-every-modern-processor-has-unfixable-security-flaws/ this page] and Compute Canada personnel are currently patching all of the systems vulnerable to attacks based on these bugs. What sort of performance degradation users will observe as a consequence of the patches is dependent on the software you are using and how it interacts with the operating system but in general the more filesystem activity and other input/output operations that a program performs during its execution, the more likely it is to suffer from a slowdown. Some benchmarks of the performance loss for AI and machine learning codes are publicly [https://medium.com/implodinggradients/meltdown-c24a9d5e254e available] and we recommend that users consider running some simple tests of their own to see if there is any substantial loss of performance with their own code(s). | ||