CPU Load Correlates with Power Consumption
Faster Software is Greener Software
Simply put, when one software product is more efficient than another, it runs faster and takes less time to solve the same problem. The less time software takes to run, the less power is consumed.
By way of illustration, consider the efficiency of a steam ship going from New York to San Fransisco before and after the Panama Canal was built. The canal was a technological marvel of its time, and it cut the journey distance from 13,000 miles to 5,000. It cut travel time by (more than) half, and reduced the journey’s coal consumption by 50%. The same work was performed, with the same “hardware” (the steamer), but in just 30 days rather than 60, and using half the fuel.
Faster run time is the most significant and most visible component of green software, but it is not the only significant factor. Other factors affecting how much power software consumes include:
- Cache miss rate
- Streamlined versus bloated, crufty software
- Use of best-suited hardware resources
- Algorithm scalability
Without getting too technical, I’ll briefly touch on each bullet point. A cache hit is when a CPU finds the information it needs in its internal cache memory, while a cache miss is when the CPU must send an off-chip request to the computer’s RAM to get the required data. A cache miss is about 100x slower than a cache hit, in part because the data has to travel about 10cm for a cache miss, versus about 5mm for a cache hit. The difference in power consumption between a cache hit and a cache miss easily be 20x to 100X, or more.
Most software starts out reasonably streamlined. Later, if the software is popular, comes a time when enhancement requests and bug reports come in faster than developers can implement them in a streamlined manner. Consequently many developers implement quick but inefficient fixes. Often this behavior is encouraged by managers trying to hit aggressive schedule commitments. The developers have intentions to come back and improve the code, but frequently their workload doesn’t permit that. After a while developers forget where the software “kludges” or hack are. Even worse, the initial developers either get reassigned to other projects or leave for other jobs. The new software developers are challenged learn the unfamiliar code and implement fixes and enhancements — adding their own cruft. This is how crufty, bloated software emerges: overworked developers, focused on schedule over software efficiency, and developer turnover.
Modern CPUs have specialized instructions and hardware for different compute operations. One example is Intel SSE technology which features a variety of Single-Instruction, Multiple-Data (SIMD) extensions. For example, SSE4 (and AVX) can add 4 or more pairs of numbers (2 4-number vectors) in one operation, rather than 4 separate ADD operations. This reduces CPU instruction traffic and saves power and time.
Finally algorithm scalability is increasingly important to modern computing and compute efficiency. Scalability has many meanings, but I will focus on the ability of software to use multiple compute resources in parallel. [Also known as parallel computing.] Unfortunately most software in use today has limited or no compute-resource scalability. This means that this software can only use 1 core of a modern 4-core CPU. In contrast, linearly-scalable software could run 3x faster by using 3 of the 4 cores at full speed. Even better, it could run 3x faster on 4 cores running at 75% speed, and consume about 30% less power. [I’ll spare you the math, but if you are curious this link will get you started.]
“Distributed Software” is Greener
Distributed computing is technology that allows compute jobs to be distributed into the “cloud” or data center queue. Rather than having desktop workstations sitting idle much of the day, a data center is a room full of computers that direct compute jobs to the least busy computers. Jobs can be directed to the computers best-suited to a particular compute request. Intelligent data centers can even put unused computers into “deep sleep” mode that uses very little power.
I use the term distributed software to mean software that is easily integrated with a job-submission or queuing software infrastructure. [Short for distributed-computing-capable software.] Clearly distributed software benefits directly from the efficiencies of a given data center. Distributed software can also benefit from the ability to run in parallel on multiple machines. The more tightly-coupled with the capabilities and status of the data center, the more efficiently distributed software can adapt to dynamic changes.
Sigma1 Software is Green
Sigma1 financial software (code-named HAL0) has been designed from the ground up to be lean and green. First and foremost, HAL0 (code-named in honor of Arthur C. Clarke’s HAL 9000 — “H-A-L is derived from Heuristic ALgorithmic (computer)”) is architected to scale near-linearly to tens or hundreds of cores, “sockets”, or distributed machines. Second, the central kernel or engine is designed to be as light-weight and streamlined as possible — helping to reduce expensive cache misses. Third, HAL0 uses Heuristic Algorithms and other “AI” features to efficiently navigate astronomically-large search spaces (10^18 and higher). Fourth, HAL0 uses an innovative computation cache system that allows repeated complex computations to be looked up in the cache, rather than recomputed. In alpha testing, this feature alone accounted for a 3X run-time improvement. Finally, HAL0 portfolio software incorporates a number of more modest run-time and power-saving features such as coding vector operations explicitly as vector operations, thus allowing easier use of SIMD and possibly GPGPU instructions and hardware.
Some financial planners still use Microsoft Excel to construct and optimize portfolios. This is slow and inefficient — to say the least. Other portfolio software I have read about is an improvement over Excel, but doesn’t mention scalability nor heuristic algorithms. It is possible, perhaps likely, that other financial software with some the capabilities of HAL0 exists. I suspect, however, that if it does, it is proprietary, in-house software that is not for sale.
A Plea for Better, Greener Software
In closing, I’d like the software community to consider how the efficiency (or inefficiency) of their current software products contribute to world-wide power consumption. Computer hardware has made tremendous strides to improving performance/power in the last ten years, and continues to do so. IT and data-center technology is also becoming more power efficient. Unfortunately, most software has been trending in the opposite direction — becoming more bloated and less efficient. I urge software developers and software managers to consider the impact of the software they are developing. I challenge you to consider, probably for the first time, how many kilowatt- or megawatt-hours your current software is likely to consume. Then ask yourself, “How can I reduce that power?”
Balhiser Financial 