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2024.03.14 #104

Closed seanmcilroy29 closed 5 months ago

seanmcilroy29 commented 6 months ago

2024.03.14 Agenda/Minutes

Time 1600 (GMT) - See the time in your timezone

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Agenda

Use case Submission Review

GreenAI - Discussion Continued - Henry / Thomas

WG Project - Dashboard

Project Review updates

ISO - Sean

Articles

For Review

Note: WG use case template submission - After submitting this issue, your use case will be submitted to the WG Agenda for discussion. Article submission—Once you submit this issue, it will be assigned to the GSF Editor for review.

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navveenb commented 6 months ago

Attended

GadhuNTTDATA commented 6 months ago

Attended

marcoshidalgonunes-avanade commented 6 months ago

Attended

srini1978 commented 6 months ago

Attended

PindyBhullar commented 6 months ago

Attended

Henry-WattTime commented 6 months ago

Attended

MichaelMULLER commented 6 months ago

Attended

pgregrivera commented 6 months ago

Attended

tmcclell commented 6 months ago

Attended

seanmcilroy29 commented 6 months ago

attended

seanmcilroy29 commented 6 months ago

MoM Henry opens the meeting at 1600 (GMT)

Green software development and carbon emissions reduction. Greg Rivera and Michael Emmanuelle discuss their product Cast Highlight, which analyzes source code to identify inefficient and carbon-emitting software patterns. Researchers used the SCI formula to calculate carbon emissions savings after optimizing the application code. Member highlights three specific patterns for improving application performance and reducing emissions, with ten occurrences.

Improving AI model efficiency and carbon awareness. Greg confirmed that hardware efficiency and carbon awareness were considered in the final calculation for ACI experiments.

Software carbon footprint measurement and reduction. The experimenters are hesitant to make any conclusions regarding Azure or Amazon because of a lack of alignment with their goals. Pindy suggests that providing information on both hardware and software used for SCI calculation would help in increasing transparency and understanding of the baseline and improvement. Pindy also suggests using the green label application comparison in the SCR project group, which could potentially lead to a broader discussion.

Efficient code patterns and their impact on energy consumption. Tammy finds the inefficient code patterns interesting and wants to know more about their impact on hardware.

Improving application performance and reducing carbon emissions. The group discussed how to interpret data on improved function duration after fixing ten deficiencies and predicted a potential 5% improvement with all fixes applied. The study found a 5% improvement in execution duration after fixing Green deficiencies, leading to a potential reduction in CO2 emissions.

Carbon emissions estimation using software. Tammy asks about the AI used for the carbon EQ plugin project and how it was developed. Tammy asks about using three years as a baseline, and Greg explains that they used four years as a default value in their calculation. Greg shares that they found a reliable statistic on cloud environments, which they used as a proxy for reduction in energy consumption. The company's software estimates carbon emissions savings by analyzing source code and augmenting with other factors. The team tested their application on different hardware configurations to measure performance improvements.

Sustainable energy and carbon neutrality in software development. A company in Germany is exploring an alternative approach to software development to address concerns about CPU utilization. Greg suggests using carbon-aware SDK for energy collection, but it's not yet implemented.

Renewable energy transition and carbon footprint. During a meeting, Greg made sure to avoid oversimplifying the timeline for the renewable energy transition. He clarified any confusion that may have arisen. The marketing team plans to share a case study with GSF using a new process that includes technical and publicly accessible documents. During the same meeting, Henry and another speaker discussed a food platform experiment that uses AI to automate fixes and measure consumption. Henry asked for input on pressing project updates and mentioned an LCA document. However, due to time constraints, the conversation had to be cut short.

Lifecycle assessment and software applications. The document compares LCA and NCAA methods for software emissions assessment, with a focus on sustainability in AI development. Henry expresses gratitude to everyone who read and commented on an article regarding the measurement of the energy and emissions intensity of AI books. The article received some exciting concepts and endorsement from the Senate. Furthermore, Data Architect Phil Hudson discusses the creation of consumption and utilization calculators for UBS on-prem systems. This process aims to identify hotspots of end-consumption and optimize service usage.

Action Items [ ] Share detailed calculation slides from Cast case study with the working group. [ ] Send calculation slides to Sean for case study publication. [ ] Reach out to Namrata to schedule an interview and overview of the Cast case study for publication. [ ] Share the LCA mapping to software applications draft article with the WG for review before publication.

pgregrivera commented 6 months ago

Sean,

Great notes, thanks.

One important clarification: Our company name is “CAST” and we are in a category called “software intelligence”.

“Cast AI” is a different company. 😊

We’ll be in touch with the follow up materials.

-Greg

Greg Rivera | VP CAST Highlight Product Marketing | CAST | Software Intelligencehttps://www.castsoftware.com/ | M +1 203 253 9036

From: Sean Mcilroy @.> Sent: Friday, March 15, 2024 7:19 AM To: Green-Software-Foundation/standards-wg @.> Cc: Greg Rivera @.>; Comment @.> Subject: Re: [Green-Software-Foundation/standards-wg] 2024.03.14 (Issue #104)

MoM Henry opens the meeting at 1600 (GMT)

Green software development and carbon emissions reduction. Greg Rivera and Michael Emmanuelle discuss their product Cast Highlight, which analyzes source code to identify inefficient and carbon-emitting software patterns. Researchers used the SCI formula to calculate carbon emissions savings after optimizing the application code. Member highlights three specific patterns for improving application performance and reducing emissions, with ten occurrences.

Improving AI model efficiency and carbon awareness. Greg confirmed that hardware efficiency and carbon awareness were considered in the final calculation for ACI experiments.

Software carbon footprint measurement and reduction. The experimenters are hesitant to make any conclusions regarding Azure or Amazon because of a lack of alignment with their goals. Pindy suggests that providing information on both hardware and software used for SCI calculation would help in increasing transparency and understanding of the baseline and improvement. Pindy also suggests using the green label application comparison in the SCR project group, which could potentially lead to a broader discussion.

Efficient code patterns and their impact on energy consumption. Tammy finds the inefficient code patterns interesting and wants to know more about their impact on hardware.

Improving application performance and reducing carbon emissions. The group discussed how to interpret data on improved function duration after fixing ten deficiencies and predicted a potential 5% improvement with all fixes applied. The study found a 5% improvement in execution duration after fixing Green deficiencies, leading to a potential reduction in CO2 emissions.

Carbon emissions estimation using software. Tammy asks about the AI used for the carbon EQ plugin project and how it was developed. Tammy asks about using three years as a baseline, and Greg explains that they used four years as a default value in their calculation. Greg shares that they found a reliable statistic on cloud environments, which they used as a proxy for reduction in energy consumption. The company's software estimates carbon emissions savings by analyzing source code and augmenting with other factors. The team tested their application on different hardware configurations to measure performance improvements.

Sustainable energy and carbon neutrality in software development. A company in Germany is exploring an alternative approach to software development to address concerns about CPU utilization. Greg suggests using carbon-aware SDK for energy collection, but it's not yet implemented.

Renewable energy transition and carbon footprint. During a meeting, Greg made sure to avoid oversimplifying the timeline for the renewable energy transition. He clarified any confusion that may have arisen. The marketing team plans to share a case study with GSF using a new process that includes technical and publicly accessible documents. During the same meeting, Henry and another speaker discussed a food platform experiment that uses AI to automate fixes and measure consumption. Henry asked for input on pressing project updates and mentioned an LCA document. However, due to time constraints, the conversation had to be cut short.

Lifecycle assessment and software applications. The document compares LCA and NCAA methods for software emissions assessment, with a focus on sustainability in AI development. Henry expresses gratitude to everyone who read and commented on an article regarding the measurement of the energy and emissions intensity of AI books. The article received some exciting concepts and endorsement from the Senate. Furthermore, Data Architect Phil Hudson discusses the creation of consumption and utilization calculators for UBS on-prem systems. This process aims to identify hotspots of end-consumption and optimize service usage.

Action Items [ ] Share detailed calculation slides from Cast AI green impact case study with the working group. [ ] Send calculation slides to Sean for case study publication. [ ] Reach out to Namrata to schedule interview and overview of the Cast AI case study for publication. [ ] Share the LCA mapping to software applications draft article with the WG for review before publication.

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