Profiling a warehouse-scale computer

With the increasing prevalence of warehouse-scale (WSC) and cloud computing, understanding the interactions of server applications with the underlying microarchitecture becomes ever more important in order to extract maximum performance out of server hardware. To aid such understanding, this paper p...

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Published in:Proceedings - International Symposium on Computer Architecture pp. 158 - 169
Main Authors: Kanev, Svilen, Darago, Juan Pablo, Hazelwood, Kim, Ranganathan, Parthasarathy, Moseley, Tipp, Wei, Gu-Yeon, Brooks, David
Format: Conference Proceeding
Language:English
Published: IEEE 13.06.2015
Subjects:
Computers
Google
Kernel
Lapping
Sensors
System-on-chip
ISSN:1063-6897
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Abstract With the increasing prevalence of warehouse-scale (WSC) and cloud computing, understanding the interactions of server applications with the underlying microarchitecture becomes ever more important in order to extract maximum performance out of server hardware. To aid such understanding, this paper presents a detailed microarchitectural analysis of live datacenter jobs, measured on more than 20,000 Google machines over a three year period, and comprising thousands of different applications. We first find that WSC workloads are extremely diverse, breeding the need for architectures that can tolerate application variability without performance loss. However, some patterns emerge, offering opportunities for co-optimization of hardware and software. For example, we identify common building blocks in the lower levels of the software stack. This "datacenter tax" can comprise nearly 30% of cycles across jobs running in the fleet, which makes its constituents prime candidates for hardware specialization in future server systems-on-chips. We also uncover opportunities for classic microarchitectural optimizations for server processors, especially in the cache hierarchy. Typical workloads place significant stress on instruction caches and prefer memory latency over bandwidth. They also stall cores often, but compute heavily in bursts. These observations motivate several interesting directions for future warehouse-scale computers.
AbstractList With the increasing prevalence of warehouse-scale (WSC) and cloud computing, understanding the interactions of server applications with the underlying microarchitecture becomes ever more important in order to extract maximum performance out of server hardware. To aid such understanding, this paper presents a detailed microarchitectural analysis of live datacenter jobs, measured on more than 20,000 Google machines over a three year period, and comprising thousands of different applications. We first find that WSC workloads are extremely diverse, breeding the need for architectures that can tolerate application variability without performance loss. However, some patterns emerge, offering opportunities for co-optimization of hardware and software. For example, we identify common building blocks in the lower levels of the software stack. This "datacenter tax" can comprise nearly 30% of cycles across jobs running in the fleet, which makes its constituents prime candidates for hardware specialization in future server systems-on-chips. We also uncover opportunities for classic microarchitectural optimizations for server processors, especially in the cache hierarchy. Typical workloads place significant stress on instruction caches and prefer memory latency over bandwidth. They also stall cores often, but compute heavily in bursts. These observations motivate several interesting directions for future warehouse-scale computers.
Author Ranganathan, Parthasarathy
Darago, Juan Pablo
Kanev, Svilen
Brooks, David
Moseley, Tipp
Wei, Gu-Yeon
Hazelwood, Kim
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Snippet With the increasing prevalence of warehouse-scale (WSC) and cloud computing, understanding the interactions of server applications with the underlying...
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SubjectTerms Computers
Google
Kernel
Lapping
Sensors
System-on-chip
Title Profiling a warehouse-scale computer
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