LASER: Light, Accurate Sharing dEtection and Repair

Abstract

Contention for shared memory, in the forms of true sharing and false sharing, is a challenging performance bug to discover and to repair. Understanding cache contention requires global knowledge of the program's actual sharing behavior, and can even arise invisibly in the program due to the opaque decisions of the memory allocator. Previous schemes have focused only on false sharing, and impose significant performance penalties or require non-trivial alterations to the operating system or runtime system environment. This paper presents the Light, Accurate Sharing dEtection and Repair (LASER) system, which leverages new performance counter capabilities available on Intel's Haswell architecture that identify the source of expensive cache coherence events. Using records of these events generated by the hardware, we build a system for online contention detection and repair that operates with low performance overhead and does not require any invasive program, compiler or operating system changes. Our experiments show that LASER imposes just 2% average runtime overhead on the Phoenix, Parsec and Splash2x benchmarks. LASER can automatically improve the performance of programs by up to 19% on commodity hardware.

Publication
In proceedings of the 22nd International Symposium on High Performance Computer Architecture (Acceptance rate: 53/240 = 22%)
Akshitha Sriraman
Akshitha Sriraman
PhD Candidate

Akshitha Sriraman is a PhD candidate in Computer Science and Engineering at the University of Michigan. Her dissertation research is on the topic of enabling hyperscale web services. Specifically, her work bridges computer architecture and software systems, demonstrating the importance of that bridge in realizing efficient hyperscale web services via solutions that span the systems stack. Her systems solutions to improve hardware efficiency have been deployed in real hyperscale data centers and currently serve billions of users, saving millions of dollars and significantly reducing the global carbon footprint. Additionally, her hardware design proposals have influenced the design of Intel’s Alder Lake (Golden Cove and future generation) CPU architectures. Akshitha has been recognized with a Facebook Fellowship, a Rackham Merit Ph.D. Fellowship, and a CIS Full-Tuition Scholarship. She was selected for the Rising Stars in EECS Workshop and the Heidelberg Laureate Forum. Her research has been recognized with an IEEE Micro Top Picks distinction and has appeared in top computer architecture and systems venues like OSDI, ISCA, ASPLOS, MICRO, and HPCA.