Title: Mosaic Pages: Increasing TLB Reach with Reduced Associativity Memory
Speaker: Donald E. Porter
Abstract: The TLB is increasingly a bottleneck for big data applications. In most designs, the number of TLB entries are highly constrained by latency requirements and growing much more slowly than the working sets of applications. Many solutions to this problem, such as huge pages, perforated pages, or TLB coalescing, rely on physical contiguity for performance gains, yet the cost of defragmenting memory can easily nullify these gains. This talk introduces mosaic pages, which increase TLB reach by compressing multiple, discrete translations into one TLB entry. Mosaic leverages virtual contiguity for locality but does not use physical contiguity. Mosaic relies on recent advances in hashing theory to constrain memory mappings, in order to realize this physical address compression without reducing memory utilization or increasing swapping. Our results show that Mosaic’s constraints on memory mapping do not harm performance and reduce TLB misses in several workloads by 6-81%.
Short Bio: Don Porter is a Professor of Computer Science at the University of North Carolina at Chapel Hill. Porter’s research interests broadly involve developing more efficient and secure computer systems. Porter earned a Ph.D. and M.S. from The University of Texas at Austin, and a B.A. from Hendrix College. He has received awards including the NSF CAREER Award, the Bert Kay Outstanding Dissertation Award from UT Austin, an ASPLOS Distinguished Paper Award in 2023, an ASPLOS Influential Paper Award in 2022, and Best Paper Awards at FAST 2016, EuroSys 2016, and RTNS 2018. Don Porter is a Professor of Computer Science at the University of North Carolina at Chapel Hill. Porter’s research interests broadly involve developing more efficient and secure computer systems. Porter earned a Ph.D. and M.S. from The University of Texas at Austin, and a B.A. from Hendrix College. He has received awards including the NSF CAREER Award, the Bert Kay Outstanding Dissertation Award from UT Austin, an ASPLOS Distinguished Paper Award in 2023, an ASPLOS Influential Paper Award in 2022, and Best Paper Awards at FAST 2016, EuroSys 2016, and RTNS 2018.
