Genome Spot

There have been a few papers recently on “green computing”, and changes we can make to ensure our work is more sustainable. The most important aspect to this is the overuse of energy in conducting our analysis, especially if the energy is derived from burning fossil fuels. What I want to do here is to put in context the energy expenditure of research computing systems by comparing it to other energy expenditures such as travel and transport. Energy consumption of a workstation or small server In order to quantify the energy expenditure of a compute system, we need to make some assumptions. We will assume that for bioinformatics, the CPU is the main consumer of power, is working at 50% of capacity. AMD Ryzen 9 5950X (16 cores / 32 threads) Maximum power consumption = 142 W /2 = 71 W source High end motherboard: 75 W. RAM stick: 3 W * 8 = 24 W Mid range graphics card idle = 12 W HDD Storage: 9 W * 2 = 18 W Total: 71 + 75 + 24 + 12 + 18 = 200 W So a workstation, or small server with 32 th

I’ve been thinking a lot about best practices lately. I even co-wrote a best practices article just last month. As I have been working with students and colleagues and reflecting on my own practices I have come to the conclusion that us researchers need to align our work more closely towards software developers rather than other researchers. In software development there is a strong differentiation between “development” and “production” work environments. Production is the live app after release to consumers, it is critical that the app functions as expaected, is reliable, useful and with a good user experience. This is after all, where these tech companies make their money. Any downtime is going to be embarrassing and will cost the company money and customers. The development environment on the other hand is the place where software developers can experiment with creating new features, prototype, and refine ideas. As things are built, the software code contains lots of bugs, and this