HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render Application Architecture ▸ Voltage/frequency scaling on general-purpose processors
HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render Application Inputs Architecture ▸ Voltage/frequency scaling on general-purpose processors
and Libraries HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render Application Inputs Architecture ▸ Voltage/frequency scaling on general-purpose processors
and Libraries HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render Application Inputs Architecture ▸ Voltage/frequency scaling on general-purpose processors ▸ End of Dennard Scaling! ▸ Diminishing return
and Libraries HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render Application Inputs Architecture WebCore Web-specific Architecture
HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render Application Inputs Architecture ▸ Lost page-level diversity ▸ Lost user QoS requirements WebCore Web-specific Architecture
HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render Application Architecture ▸ Lost page-level diversity ▸ Lost user QoS requirements WebCore Web-specific Architecture
HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render Application Architecture WebCore Web-specific Architecture GreenWeb Language Extensions Runtime
QoS information ▸ Runtime scheduling mechanisms to exploit heterogeneous hardware ▸ Hardware accelerators specialized for the key computation kernel Future mobile Web systems can achieve energy-efficiency without sacrificing responsiveness by incorporating:
50 75 100 Mobile Desktop Data Center Never/Rarely Sometimes Often/Almost Always “My applications have requirements about energy usage.” [ICSE 2016] Manotas et al., “An Empirical Study of Practitioners’ Perspectives on Green Software Engineering”
50 75 100 Mobile Desktop Data Center Never/Rarely Sometimes Often/Almost Always “My applications have requirements about energy usage.” [ICSE 2016] Manotas et al., “An Empirical Study of Practitioners’ Perspectives on Green Software Engineering”
50 75 100 Mobile Desktop Data Center Never/Rarely Sometimes Often/Almost Always “My applications have requirements about energy usage.” [ICSE 2016] Manotas et al., “An Empirical Study of Practitioners’ Perspectives on Green Software Engineering”
25 50 75 100 Mobile Never/Rarely Sometimes Often/Almost Always “I'm willing to sacrifice performance, etc. for reduced energy usage.” [ICSE 2016] Manotas et al., “An Empirical Study of Practitioners’ Perspectives on Green Software Engineering”
25 50 75 100 Mobile Never/Rarely Sometimes Often/Almost Always “I'm willing to sacrifice performance, etc. for reduced energy usage.” [ICSE 2016] Manotas et al., “An Empirical Study of Practitioners’ Perspectives on Green Software Engineering”
constraints ▸ Result in 60% energy savings on real hardware/software implementations GreenWeb: Language for Energy-Efficiency ▸ Language abstractions for expressing QoS
*/ } </script> </head> <body> <div ontouchend=“animateMove()”> <div/> <!— other elements --> </body> </html> 26 Expressing Mobile Web QoS element event
*/ } </script> </head> <body> <div ontouchend=“animateMove()”> <div/> <!— other elements --> </body> </html> 26 Expressing Mobile Web QoS element event
*/ } </script> </head> <body> <div ontouchend=“animateMove()”> <div/> <!— other elements --> </body> </html> 26 Expressing Mobile Web QoS Expressing QoS at an event granularity element event
</script> </head> <body> <div ontouchend=“animateMove()”> <div/> <!— other elements --> </body> </html> <style> </style> <html> <head> 27 Expressing Mobile Web QoS Annotation element event
</script> </head> <body> <div ontouchend=“animateMove()”> <div/> <!— other elements --> </body> </html> <style> </style> <html> <head> 27 Expressing Mobile Web QoS Annotation element event div { } ontouchend
top 2,500 webpages Model Construction & Refinement Refine the linear model Predictors (HTML, CSS) Responses (Time, Energy) Mitigate Over-fitting Model Non-Linearity Linear Regression
top 2,500 webpages Model Construction & Refinement Refine the linear model Model Validation Validating on another 2,500 webpages Predictors (HTML, CSS) Responses (Time, Energy) Mitigate Over-fitting Model Non-Linearity Linear Regression Loading Time Model Energy Model
Tmemory + Ncycles / f [PLDI 2003] Xie, et al., “Compile-Time Dynamic Voltage Scaling Settings: Opportunities and Limits” Energy = Execution Time x Power
an Exynos 5410 SoC used in Samsung Galaxy S4. Little core cluster: ARM Cortex A7, In-order with 2 issue Big core cluster: ARM Cortex A15, OoO with 3 issue Overhead: ▸ Frequency switch: 100 us ▸ Core migration: 20 us
an Exynos 5410 SoC used in Samsung Galaxy S4. Implementation incorporated into Chrome running on Android. Little core cluster: ARM Cortex A7, In-order with 2 issue Big core cluster: ARM Cortex A15, OoO with 3 issue Overhead: ▸ Frequency switch: 100 us ▸ Core migration: 20 us
an Exynos 5410 SoC used in Samsung Galaxy S4. Implementation incorporated into Chrome running on Android. UI-level record and replay for reproducibility. [ISPASS’15] Little core cluster: ARM Cortex A7, In-order with 2 issue Big core cluster: ARM Cortex A15, OoO with 3 issue Overhead: ▸ Frequency switch: 100 us ▸ Core migration: 20 us
3.0 CamanJS Craigslist Paperjs Goo Google Todo CNet BBC LZMA-JS Amazon W3School MSN Norm. Energy 0.0 0.3 0.5 0.8 1.0 CamanJS Craigslist Paperjs Goo Google Todo CNet BBC LZMA-JS Amazon W3School MSN GreenWeb Interactive Perf
in mobile Web computing Abstraction Express QoS constraints Runtime Satisfy QoS specifications using energy saving techniques Effect Significant energy savings
29 languages ▹ c.f., H264 codec: 0.13M LoC, 6 languages ‣Code base is very irregular ▹ Not amenable to traditional specialization General-Purpose Designs WebCore: a Web-Specific Mobile Architecture
matchedRules) { for (each property in rule) { switch (property.id) { case Font: Style[Font] = Handler(property.value, DOMNode); break; case N: ...}}} Rule-level Parallelism (RLP) Property-level Parallelism (PLP) ▸ Exploiting the parallelism to increase the arithmetic intensity ▸ Move operands closer to operations to sustain the computations
Rule i.id ... Prop m ... Prop k ... Rule j.id ... ... ... ... ... start end start end Rule i Prop k Prop m Prop m Prop l Style l Style m Style k Style Resolution Unit 61 Prop m Prop m 61 Input Scratchpad Conflict Resolution Output Scratchpad Compute Lanes
2 2.2 2.4 Energy (J) Load Time (s) ▸Fully synthesized using Synopsys 28 nm toolchain ▸Cost of specialization: 0.59 mm2 area overhead ▹ SoC die area is 122 mm2 in Samsung Galaxy S4 ▹ A15s’ area: 19 mm2
2 2.2 2.4 Energy (J) Load Time (s) A15-like design ▸Fully synthesized using Synopsys 28 nm toolchain ▸Cost of specialization: 0.59 mm2 area overhead ▹ SoC die area is 122 mm2 in Samsung Galaxy S4 ▹ A15s’ area: 19 mm2
2 2.2 2.4 Energy (J) Load Time (s) A15-like design Customization ▸Fully synthesized using Synopsys 28 nm toolchain ▸Cost of specialization: 0.59 mm2 area overhead ▹ SoC die area is 122 mm2 in Samsung Galaxy S4 ▹ A15s’ area: 19 mm2
2 2.2 2.4 Energy (J) Load Time (s) 18.6% A15-like design Customization ▸Fully synthesized using Synopsys 28 nm toolchain ▸Cost of specialization: 0.59 mm2 area overhead ▹ SoC die area is 122 mm2 in Samsung Galaxy S4 ▹ A15s’ area: 19 mm2
2 2.2 2.4 Energy (J) Load Time (s) 18.6% 22.2% A15-like design Customization ▸Fully synthesized using Synopsys 28 nm toolchain ▸Cost of specialization: 0.59 mm2 area overhead ▹ SoC die area is 122 mm2 in Samsung Galaxy S4 ▹ A15s’ area: 19 mm2
2 2.2 2.4 Energy (J) Load Time (s) 18.6% 22.2% A15-like design Customization Specialization ▸Fully synthesized using Synopsys 28 nm toolchain ▸Cost of specialization: 0.59 mm2 area overhead ▹ SoC die area is 122 mm2 in Samsung Galaxy S4 ▹ A15s’ area: 19 mm2
2 2.2 2.4 Energy (J) Load Time (s) 18.6% 22.2% 22.2% A15-like design Customization Specialization ▸Fully synthesized using Synopsys 28 nm toolchain ▸Cost of specialization: 0.59 mm2 area overhead ▹ SoC die area is 122 mm2 in Samsung Galaxy S4 ▹ A15s’ area: 19 mm2
2 2.2 2.4 Energy (J) Load Time (s) 18.6% 22.2% 9.2% 22.2% A15-like design Customization Specialization ▸Fully synthesized using Synopsys 28 nm toolchain ▸Cost of specialization: 0.59 mm2 area overhead ▹ SoC die area is 122 mm2 in Samsung Galaxy S4 ▹ A15s’ area: 19 mm2
2 2.2 2.4 Energy (J) Load Time (s) A15-like design Customization Specialization 29.2% 47.0% ▸Fully synthesized using Synopsys 28 nm toolchain ▸Cost of specialization: 0.59 mm2 area overhead ▹ SoC die area is 122 mm2 in Samsung Galaxy S4 ▹ A15s’ area: 19 mm2
Security Local Storage User Input Layout Render Franeworks and Libraries HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render Franeworks and Libraries HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render Franeworks and Libraries HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render Franeworks and Libraries HTML JavaScript CSS Language Runtime Styling Security Local Storage User Input Layout Render
of Mobile Web Computing” [PLDI 2016] Yuhao Zhu, Vijay Janapa Reddi, “GreenWeb: Language Extensions for Energy-Efficient Mobile Web Computing” [HPCA 2015] Yuhao Zhu, Matthew Halpern, Vijay Janapa Reddi, “Event- Based Scheduling for Energy-Efficient QoS (eQoS) in Mobile Web Applications” [HPCA 2013] Yuhao Zhu, Vijay Janapa Reddi, “High-Performance and Energy-Efficient Mobile Web Browsing on Big/Little Systems” [CAL 2012] Yuhao Zhu, Aditya Srikanth, Jingwen Leng, Vijay Janapa Reddi, “Exploiting Webpage Characteristics for Energy-Efficient Mobile Web Browsing” (Best of CAL) [ISCA 2014] Yuhao Zhu, Vijay Janapa Reddi, “WebCore: Architectural Support for Mobile Web Browsing” [IEEE MICRO 2015] Yuhao Zhu, Matthew Halpern, Vijay Janapa Reddi, “The Role of the CPU in Energy-Efficient Mobile Web Browsing” [HPCA 2016] Matthew Halpern, Yuhao Zhu, Vijay Janapa Reddi, “Mobile CPU’s Rise to Power: Quantifying the Impact of Generational Mobile CPU Design Trends on Performance, Energy, and User Satisfaction” GreenWeb WebRT WebCore Motivational Studies Future Web
Integrated CPU/GPU Microarchitecture for IP Routing.” [DAC 2010] Bo Wang, Yuhao Zhu, Yangdong Deng, “Distributed Time, Conservative Parallel Logic Simulation on GPUs.” [TODAES 2011] Yuhao Zhu, Bo Wang, Yangdong Deng, “Massively Parallel Logic Simulation with GPUs.” [ISPASS 2015] Matthew Halpern, Yuhao Zhu, Ramesh Peri, and Vijay Janapa Reddi, “Mosaic: Cross-platform User-interaction Record and Replay for the Fragmented Android Ecosystem.” [IRPS 2014] Chen Zhou, Xiaofei Wang, Weichao Xu, Yuhao Zhu, Vijay Janapa Reddi, Chris Kim, “Estimation of Instantaneous Frequency Fluctuation in a Fast DVFS Environment Using an Empirical BTI Stress- Relaxation Model.” GPGPU & IP Routing Architecture Tools Reliability [MICRO 2015] Yuhao Zhu, Daniel Richins, Matthew Halpern, Vijay Janapa Reddi, “Microarchitectural Implications of Event-driven Server- side Web Applications” (Top Picks Honorable Mention) Server Microarch
Fall 2010 A ADV EMBED MICROCONTROL SYS Mark McDermott Spring 2011 A- MEMORY MANAGEMENT Kathryn McKinley Spring 2011 Y A VLSI I Jacob Abraham Fall 2011 A- COMP ARCH: PARALLISM/LOCLTY Mattan Erez Fall 2011 A MICROARCHITECTURE Yale Patt Spring 2012 B DYNAMIC COMPILATION Vijay Janapa Reddi Spring 2012 A- COMP PERF EVAL/BENCHMARKING Lizy John Fall 2012 B+ PARALLEL COMP ARCHITECTURE Derek Chiou Spring 2013 B+ HUMAN COMPUT & CROWDSRCING Matt Lease Fall 2015 Y A-
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