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The fallacy of ‘synthetic benchmarks’
PrefaceApple's M1 has caused a lot of people to start talking about and questioning the value of synthetic benchmarks, as well other (often indirect or badly controlled) information we have about the chip and its predecessors.
I recently got in a Twitter argument with Hardware Unboxed about this very topic, and given it was Twitter you can imagine why I feel I didn't do a great job explaining my point. This is a genuinely interesting topic with quite a lot of nuance, and the answer is neither ‘Geekbench bad’ nor ‘Geekbench good’.
Note that people have M1s in hand now, so this isn't a post about the M1 per se (you'll have whatever metric you want soon enough), it's just using this announcement to talk about the relative qualities of benchmarks, in the context of that discussion.
What makes a benchmark good?A benchmark is a measure of a system, the purpose of which is to correlate reliably with actual or perceived performance. That's it. Any benchmark which correlates well is Good. Any benchmark that doesn't is Bad.
There a common conception that ‘real world’ benchmarks are Good and ‘synthetic’ benchmarks are Bad. While there is certainly a grain of truth to this, as a general rule it is wrong. In many aspects, as we'll discuss, the dividing line between ‘real world’ and ‘synthetic’ is entirely illusionary, and good synthetic benchmarks are specifically designed to tease out precisely those factors that correlate with general performance, whereas naïve benchmarking can produce misleading or unrepresentative results even if you are only benchmarking real programs. Most synthetic benchmarks even include what are traditionally considered real-world workloads, like SPEC 2017 including the time it takes for Blender to render a scene.
As an extreme example, large file copies are a real-world test, but a ‘real world’ benchmark that consists only of file copies would tell you almost nothing general about CPU performance. Alternatively, a company might know that 90% of their cycles are in a specific 100-line software routine; testing that routine in isolation would be a synthetic test, but it would correlate almost perfectly for them with actual performance.
On the other hand, it is absolutely true there are well-known and less-well-known issues with many major synthetic benchmarks.
Boost vs. sustained performanceLots of people seem to harbour misunderstandings about instantaneous versus sustained performance.
Short workloads capture instantaneous performance, where the CPU has opportunity to boost up to frequencies higher than the cooling can sustain. This is a measure of peak performance or burst performance, and affected by boost clocks. In this regime you are measuring the CPU at the absolute fastest it is able to run.
Peak performance is important for making computers feel ‘snappy’. When you click an element or open a web page, the workload takes place over a few seconds or less, and the higher the peak performance, the faster the response.
Long workloads capture sustained performance, where the CPU is limited by the ability of the cooling to extract and remove the heat that it is generating. Almost all the power a CPU uses ends up as heat, so the cooling determines an almost completely fixed power limit. Given a sustained load, and two CPUs using the same cooling, where both of which are hitting the power limit defined by the quality of the cooling, you are measuring performance per watt at that wattage.
Sustained performance is important for demanding tasks like video games, rendering, or compilation, where the computer is busy over long periods of time.
Consider two imaginary CPUs, let's call them Biggun and Littlun, you might have Biggun faster than Littlun in short workloads, because Biggun has a higher peak performance, but then Littlun might be faster in sustained performance, because Littlun has better performance per watt. Remember, though, that performance per watt is a curve, and peak power draw also varies by CPU. Maybe Littlun uses only 1 Watt and Biggun uses 100 Watt, so Biggun still wins at 10 Watts of sustained power draw, or maybe Littlun can boost all the way up to 10 Watts, but is especially inefficient when doing so.
In general, architectures designed for lower base power draw (eg. most Arm CPUs) do better under power-limited scenarios, and therefore do relatively better on sustained performance than they do on short workloads.
On the Good and Bad of SPECSPEC is an ‘industry standard’ benchmark. If you're anything like me, you'll notice pretty quickly that this term fits both the ‘good’ and the ‘bad’. On the good, SPEC is an attempt to satisfy a number of major stakeholders, who have a vested interest in a benchmark that is something they, and researchers generally, can optimized towards. The selection of benchmarks was not arbitrary, and the variety captures a lot of interesting and relevant facets of program execution. Industry still uses the benchmark (and not just for marketing!), as does a lot of unaffiliated research. As such, SPEC has also been well studied.
SPEC includes many real programs, run over extended periods of time. For example, 400.perlbench runs multiple real Perl programs, 401.bzip2 runs a very popular compression and decompression program, 403.gcc tests compilation speed with a very popular compiler, and 464.h264ref tests a video encoder. Despite being somewhat aged and a bit light, the performance characteristics are roughly consistent with the updated SPEC2017, so it is not generally valid to call the results irrelevant from age, which is a common criticism.
One major catch from SPEC is that official benchmarks often play shenanigans, as compilers have found ways, often very much targeted towards gaming the benchmark, to compile the programs in a way that makes execution significantly easier, at times even because of improperly written programs. 462.libquantum is a particularly broken benchmark. Fortunately, this behaviour can be controlled for, and it does not particularly endanger results from AnandTech, though one should be on the lookout for anomalous jumps in single benchmarks.
A more concerning catch, in this circumstance, is that some benchmarks are very specific, with most of their runtime in very small loops. The paper Performance Characterization of SPEC CPU2006 Integer Benchmarks on x86-64 Architecture (as one of many) goes over some of these in section IV. For example, most of the time in 456.hmmer is in one function, and 464.h264ref's hottest loop contains many repetitions of the same line. While, certainly, a lot of code contains hot loops, the performance characteristics of those loops is rarely precisely the same as for those in some of the SPEC 2006 benchmarks. A good benchmark should aim for general validity, not specific hotspots, which are liable to be overtuned.
SPEC2006 includes a lot of workloads that make more sense for supercomputers than personal computers, such as including lots of Fortran code and many simulation programs. Because of this, I largely ignore the SPEC floating point; there are users for whom it may be relevant, but not me, and probably not you. As another example, SPECfp2006 includes the old rendering program POV-Ray, which is no longer particularly relevant. The integer benchmarks are not immune to this overspecificity; 473.astar is a fairly dated program, IMO. Particularly unfortunate is that many of these workloads are now unrealistically small, and so can almost fit in some of the larger caches.
SPEC2017 makes the great decision to add Blender, as well as updating several other programs to more relevant modern variants. Again, the two benchmarks still roughly coincide with each other, so SPEC2006 should not be altogether dismissed, but SPEC2017 is certainly better.
Because SPEC benchmarks include disaggregated scores (as in, scores for individual sub-benchmarks), it is easy to check which scores are favourable. For SPEC2006, I am particularly favourable to 403.gcc, with some appreciation also for 400.perlbench. The M1 results are largely consistent across the board; 456.hmmer is the exception, but the commentary discusses that quirk.
(and the multicore metric)SPEC has a ‘multicore’ variant, which literally just runs many copies of the single-core test in parallel. How workloads scale to multiple cores is highly test-dependent, and depends a lot on locks, context switching, and cross-core communication, so SPEC's multi-core score should only be taken as a test of how much the chip throttles down in multicore workloads, rather than a true test of multicore performance. However, a test like this can still be useful for some datacentres, where every core is in fact running independently.
On the Good and Bad of GeekbenchGeekbench does some things debatably, some things fairly well, and some things awfully. Let's start with the bad.
To produce the aggregate scores (the final score at the end), Geekbench does a geometric mean of each of the two benchmark groups, integer and FP, and then does a weighted arithmetic mean of the crypto score with the integer and FP geometric means, with weights 0.05, 0.65, and 0.30. This is mathematical nonsense, and has some really bad ramifications, like hugely exaggerating the weight of the crypto benchmark.
Secondly, the crypto benchmark is garbage. I don't always agree with his rants, but Linus Torvald's rant is spot on here: https://www.realworldtech.com/forum/?threadid=196293&curpostid=196506. It matters that CPUs offer AES acceleration, but not whether it's X% faster than someone else's, and this benchmark ignores that Apple has dedicated hardware for IO, which handles crypto anyway. This benchmark is mostly useless, but can be weighted extremely high due to the score aggregation issue.
Consider the effect on these two benchmarks. They are not carefully chosen to be perfectly representative of their classes.
Unfortunately, many of the workloads beyond just AES are pretty questionable, as many are unnaturally simple. It's also hard to characterize what they do well; the SQLite benchmark could be really good, if it was following realistic usage patterns, but I don't think it is. Lots of workloads, like the ray tracing one, are good ideas, but the execution doesn't match what you'd expect of real programs that do that work.
Note that this is not a criticism of benchmark intensity or length. Geekbench makes a reasonable choice to only benchmark peak performance, by only running quick workloads, with gaps between each bench. This makes sense if you're interested in the performance of the chip, independent of cooling. This is likely why the fanless Macbook Air performs about the same as the 13" Macbook Pro with a fan. Peak performance is just a different measure, not more or less ‘correct’ than sustained.
On the good side, Geekbench contains some very sensible workloads, like LZMA compression, JPEG compression, HTML5 parsing, PDF rendering, and compilation with Clang. Because it's a benchmark over a good breadth of programs, many of which are realistic workloads, it tends to capture many of the underlying facets of performance in spite of its flaws. This means it correlates will with, eg., SPEC 2017, even though SPEC 2017 is a sustained benchmark including big ‘real world’ programs like Blender.
To make things even better, Geekbench is disaggregated, so you can get past the bad score aggregation and questionable benchmarks just by looking at the disaggregated scores. In the comparison before, if you scroll down you can see individual scores. M1 wins the majority, including Clang and Ray Tracing, but loses some others like LZMA and JPEG compression. This is what you'd expect given the M1 has the advantage of better speculation (eg. larger ROB) whereas the 5900X has a faster clock.
(and under Rosetta)We also have Geekbench scores under Rosetta. There, one needs to take a little more caution, because translation can sometimes behave worse on larger programs, due to certain inefficiencies, or better when certain APIs are used, or worse if the benchmark includes certain routines (like machine learning) that are hard to translate well. However, I imagine the impact is relatively small overall, given Rosetta uses ahead-of-time translation.
(and the multicore metric)Geekbench doesn't clarify this much, so I can't say much about this. I don't give it much attention.
(and the GPU compute tests)GPU benchmarks are hugely dependent on APIs and OSs, to a degree much larger than for CPUs. Geekbench's GPU scores don't have the mathematical error that the CPU benchmarks do, but that doesn't mean it's easy to compare them. This is especially true given there are only a very limited selection of GPUs with 1st party support on iOS.
None of the GPU benchmarks strike me as particularly good, in the way that benchmarking Clang is easily considered good. Generally, I don't think you should have much stock in Geekbench GPU.
On the Good and Bad of microarchitectural measuresAnandTech's article includes some of Andrei's traditional microarchitectural measures, as well as some new ones I helped introduce. Microarchitecture is a bit of an odd point here, in that if you understand how CPUs work well enough, then they can tell you quite a lot about how the CPU will perform, and in what circumstances it will do well. For example, Apple's large ROB but lower clock speed is good for programs with a lot of latent but hard to reach parallelism, but would fair less well on loops with a single critical path of back-to-back instructions. Andrei has also provided branch prediction numbers for the A12, and again this is useful and interesting for a rough idea.
However, naturally this cannot tell you performance specifics, and many things can prevent an architecture living up to its theoretical specifications. It is also difficult for non-experts to make good use of this information. The most clear-cut thing you can do with the information is to use it as a means of explanation and sanity-checking. It would be concerning if the M1 was performing well on benchmarks with a microarchitecture that did not suggest that level of general performance. However, at every turn the M1 does, so the performance numbers are more believable for knowing the workings of the core.
On the Good and Bad of CinebenchCinebench is a real-world workload, in that it's just the time it takes for a program in active use to render a realistic scene. In many ways, this makes the benchmark fairly strong. Cinebench is also sustained, and optimized well for using a huge number of cores.
However, recall what makes a benchmark good: to correlate reliably with actual or perceived performance. Offline CPU ray tracing (which is very different to the realtime GPU-based ray tracing you see in games) is an extremely important workload for many people doing 3D rendering on the CPU, but is otherwise a very unusual workload in many regards. It has a tight rendering loop with very particular memory requirements, and it is almost perfectly parallel, to a degree that many workloads are not.
This would still be fine, if not for one major downside: it's only one workload. SPEC2017 contains a Blender run, which is conceptually very similar to Cinebench, but it is not just a Blender run. Unless the work you do is actually offline, CPU based rendering, which for the M1 it probably isn't, Cinebench is not a great general-purpose benchmark.
(Note that at the time of the Twitter argument, we only had Cinebench results for the A12X.)
On the Good and Bad of GFXBenchGFXBench, as far as I can tell, makes very little sense as a benchmark nowadays. Like I said for Geekbench's GPU compute benchmarks, these sort of tests are hugely dependent on APIs and OSs, to a degree much larger than for CPUs. Again, none of the GPU benchmarks strike me as particularly good, and most tests look... not great. This is bad for a benchmark, because they are trying to represent the performance you will see in games, which are clearly optimized to a different degree.
This is doubly true when Apple GPUs use a significantly different GPU architecture, Tile Based Deferred Rendering, which must be optimized for separately. EDIT: It has been pointed out that as a mobile-first benchmark, GFXBench is already properly optimized for tiled architectures.
On the Good and Bad of browser benchmarksIf you look at older phone reviews, you can see runs of the A13 with browser benchmarks.
Browser benchmark performance is hugely dependent on the browser, and to an extent even the OS. Browser benchmarks in general suck pretty bad, in that they don't capture the main slowness of browser activity. The only thing you can realistically conclude from these browser benchmarks is that browser performance on the M1, when using Safari, will probably be fine. They tell you very little about whether the chip itself is good.
On the Good and Bad of random application benchmarksThe Affinity Photo beta comes with a new benchmark, which the M1 does exceptionally well in. We also have a particularly cryptic comment from Blackmagicdesign, about DaVinci Resolve, that the “combination of M1, Metal processing and DaVinci Resolve 17.1 offers up to 5 times better performance”.
Generally speaking, you should be very wary of these sorts of benchmarks. To an extent, these benchmarks are built for the M1, and the generalizability is almost impossible to verify. There's almost no guarantee that Affinity Photo is testing more than a small microbenchmark.
This is the same for, eg., Intel's ‘real-world’ application benchmarks. Although it is correct that people care a lot about the responsiveness of Microsoft Word and such, a benchmark that runs a specific subroutine in Word (such as conversion to PDF) can easily be cherry-picked, and is not actually a relevant measure of the slowness felt when using Word!
This is a case of what are seemingly ‘real world’ benchmarks being much less reliable than synthetic ones!
On the Good and Bad of first-party benchmarksOf course, then there are Apple's first-party benchmarks. This includes real applications (Final Cut Pro, Adobe Lightroom, Pixelmator Pro and Logic Pro) and various undisclosed benchmark suites (select industry-standard benchmarks, commercial applications, and open source applications).
I also measured Baldur's Gate 3 in a talk running at ~23-24 FPS at 1080 Ultra, at the segment starting 7:05. https://developer.apple.com/videos/play/tech-talks/10859
Generally speaking, companies don't just lie in benchmarks. I remember a similar response to NVIDIA's 30 series benchmarks. It turned out they didn't lie. They did, however, cherry-pick, specifically including benchmarks that most favoured the new cards. That's very likely the same here. Apple's numbers are very likely true and real, and what I measured from Baldur's Gate 3 will be too, but that's not to say other, relevant things won't be worse.
Again, recall what makes a benchmark good: to correlate reliably with actual or perceived performance. A biased benchmark might be both real-world and honest, but if it's also likely biased, it isn't a good benchmark.
On the Good and Bad of the Hardware Unboxed benchmark suiteThis isn't about Hardware Unboxed per se, but it did arise from a disagreement I had, so I don't feel it's unfair to illustrate with the issues in Hardware Unboxed's benchmarking. Consider their 3600 review.
Here are the benchmarks they gave for the 3600, excluding the gaming benchmarks which I take no issue with.
- Cinebench (MT+ST)
- V-Ray Benchmark (MT)
- Corona 1.3 Benchmark (MT)
- Blender Open Data (MT)
- WinRAR (MT)
- 7Zip File Manager (MT)
- 7Zip File Manager (MT)
- Adobe Premiere Pro video encode (MT)
To have a lineup like this and then complain about the synthetic benchmarks for M1 and the A14 betrays a total misunderstanding about what benchmarking is. There are a total of three real workloads here, one of which is single threaded. Further, that one single threaded workload is one you'll never realistically run single threaded. As discussed, offline CPU rendering is an atypical and hard to generalize workload. Compression and decompression are also very specific sorts of benchmarks, though more readily generalizable. Video encoding is nice, but this still makes for a very thin picking.
Thus, this lineup does not characterize any realistic single-threaded workloads, nor does it characterize multi-core workloads that aren't massively parallel.
Contrast this to SPEC2017, which is a ‘synthetic benchmark’ of the sort Hardware Unboxed was criticizing. SPEC2017 contains a rendering benchmark (526.blender) and a compression benchmark (557.xz), and a video encode benchmark (525.x264), but it also contains a suite of other benchmarks, chosen specifically so that all the benchmarks measure different aspects of the architecture. It includes workloads like Perl, GCC, workloads that stress different aspects of memory, plus extremely branchy searches (eg. a chess engine), image manipulation routines, etc. Geekbench is worse, but as mentioned before, it still correlates with SPEC2017, by virtue of being a general benchmark that captures most aspects of the microarchitecture.
So then, when SPEC2017 contains your workloads, but also more, and with more balance, how can one realistically dismiss it so easily? And if Geekbench correlates with SPEC2017, then how can you dismiss that, at least given disaggregated metrics?
In conclusionThe bias against ‘synthetic benchmarks’ is understandable, but misplaced. Any benchmark is synthetic, by nature of abstracting speed to a number, and any benchmark is real world, by being a workload you might actually run. What really matters is knowing how each workload is represents your use-case (I care a lot more about compilation, for example), and knowing the issues with each benchmark (eg. Geekbench's bad score aggregation).
Skepticism is healthy, but skepticism is not about rejecting evidence, it is about finding out the truth. The goal is not to have the benchmarks which get labelled the most Real World™, but about genuinely understanding the performance characteristics of these devices—especially if you're a CPU reviewer. If you're a reviewer who dismisses Geekbench, but you haven't read the Geekbench PDF characterizing the workload, or your explanation stops at ‘it's short’, or ‘it's synthetic’, you can do better. The topics I've discussed here are things I would consider foundational, if you want to characterize a CPU's performance. Stretch goals would be to actually read the literature on SPEC, for example, or doing performance counter-aided analysis of the benchmarks you run.
Normally I do a reread before publishing something like this to clean it up, but I can't be bothered right now, so I hope this is good enough. If I've made glaring mistakes (I might've, I haven't done a second pass), please do point them out.
Sakuna: Of Rice and Ruin Review Thread
Game InformationGame Title: Sakuna: Of Rice and Ruin
- Nintendo Switch (Nov 10, 2020)
- PC (Nov 10, 2020)
- PlayStation 4 (Nov 10, 2020)
Publishers: Xseed, Marvelous Europe
OpenCritic - 79 average - 77% recommended - 26 reviews
Critic ReviewsBut Why Tho? - Eva Herinkova - 7 / 10
Sakuna: Of Rice and Ruin is a fun gameplay experience if you’re really into managing statistics and growing from your mistakes. The biggest flaw is that the narrative, which has an interesting premise, is stunted by the shallowness and, at times, obnoxious nature of the characters. Luckily, Sakuna: Of Rice and Ruin is focused more on the gameplay and is an easy recommendation if you’re looking for a rewarding combat experience and farming simulator.
Sakuna: Of Rice and Ruin is a breath of fresh air that shouldn't be overlooked. It's hard to believe that a game with this much detail and depth is from just two developers. The team at Edelweiss have created such a unique game with amazing production and polished gameplay. There are a few little features like enemy repetition and lacklustre level layouts at times that could be improved. However I think Sakuna: Of Rice and Ruin is definitely one of 2020's exceptional releases.
If a quirky action game with RPG progression and relaxing agricultural activities seems like your kind of thing, trust your gut on this one. The Nintendo Switch version is solid enough for me to recommend it.
Sakuna: Of Rice and Ruin delivers a remarkably in-depth set of mechanical systems. The interplay is impressive, even if the execution is somewhat flawed.
Some accomplished character work and a narrative full of heart, sits next to a deep and detailed rice-farming mechanic that will have you sinking hours in trying to get the perfect crop. However, fiddly combat and shallow platforming take their toll. If you’re anything like me, you’ll get lost in the farming, and let the other parts lie fallow.
Sakuna: Of Rice and Ruin is a marvellous game. Pulling together two genres in a fun and unique way, you're left with a game quite unlike anything before it. Give it a shot, you'll find it more than worth the wait.
Combat is fun, and it ties into the simulation elements well. However, the pacing and repetition makes it difficult to fully appreciate it all
Sakuna: Of Rice and Ruin is a bold genre fusion that pays off with superb farming and combat systems plus a cast of characters you'll remember for a long time to come.
Sakuna: Of Rice and Ruin is definitely an interesting title, mixing the mundanities of rice planting with 2D hacking and slashing. While an acquired taste, those who like their games unique should have a fun time with Sakuna. If you can forgive an unlikeable protagonist and some repetitive gameplay at times, Sakuna is a solid title that shines even with its flaws.
This unusual take on virtual farming has you battling demons – when you're not tending to rice paddies
Sakuna: Of Rice and Ruin seamlessly blends 2D platforming action with 3D farm management.
Sakuna: Of Rice and Ruin isn’t perfect, but the good more than outweighs the bad here. It’s just frustrating for me personally, cause there were so many things about the game that could have translated to a perfect experience. There’s just too many missteps for that. Thankfully, what’s here is still very much worth the price of admission. If you want a game you can sink hours and hours into while enjoying a meandering and surprising story, you have to check this one out.
Sakuna unfolds as an original action J-RPG, that feels different and it's fun. if you connect with it, you'll find that it's hard to put it aside, even if it can fail in grind and repetition, everything it's well dosed and executed, without the usual problems found on other Nintendo Switch ports. A superb RPG surprise to finish this crazy year.
Sakuna: Of Rice and Ruin comes with a decent price tag, not as much as Nintendo’s big staples. However, it’s more than reasonable for Sakuna. In just under a month of playing, and I’m still hooked. Even as someone who generally dislikes platformers, Sakuna has ticked all the right boxes for me. My love of farming, mixed with the fighting style, makes for a complete and fascinating game. I will be recommending this game to pretty much everyone. It has a bit of most things but manages to incorporate it all smoothly. Other games can feel jarring when mixing playstyle or genres, but Marvelous have succeeded, almost expertly.
Sakuna: Of Rice and Ruin succeeds at offering both an exploration-based beat 'em up adventure and a relaxing life-sim experience. The combat may not be perfect and the difficulty feels uneven at times, but the addicting gameplay loop and charming world more than make up for it.
Now if only I could catch more frogs...
I don’t think Sakuna: Of Rice and Ruin can be classified as one single genre. It’s blending of farming and action only scrapes the surface of what this game actually offers. Still, by looking at those two pieces alone, there is a ton of excellent moments of gameplay to experience. Yes, it’s very much a farming game, and yes, it is full of action, but these two systems run seamlessly alongside a beautiful story and brilliant presentation.
Sakuna: Of Rice and Ruin is a fun and entertaining game. I was quickly swept in by its charming characters, great writing, and rice farming simulation. Unfortunately, a lot of the game mechanics hold it back including the brutal day and night cycle and having to wait an entire game year just to level up Sakuna to make the game a little easier.
In the evenings you can spend time with your new human family to chat and eat a meal using the food that you’ve gathered and grown yourself. Watching Sakuna slowly mature and start to care about more than just herself is a truly heart-warming journey. Sakuna: Of Rice and Ruin is an absolutely wonderful blend of farming simulator and action RPG. Whether you’re fighting off hordes of demons or trying to find the best way to manage your crop, there is constantly something new to learn and discover in this charmingly unique adventure.
Japanese indie game developer Edelweiss has put a lot of heart into this long-awaited game, but some key flaws hinder this charming title.
Sakuna: Of Rice and Ruin is a game that grows on you. People accustomed to farming simulations like Story of Seasons or even Rune Factory will find themselves forced to suddenly pay way more attention to the process of growing crops than before, then be patient since it will be in-game years before you “get good” at growing crops. Folks coming in because the combat seems satisfying will have to understand this is a game where constantly revisiting areas and keeping up with farming will be necessary to make any sort of significant progress. And everyone will have to deal with the fact that the lighting system and fonts will sometimes make you strain your eyes as you try to get things done.
Sakuna: Of Rice and Ruin has a lot going for it, from a fun and quirky protagonist to snappy combat and gorgeous visuals. Above all else, though, it's one of the most immersive and rewarding farming experiences in gaming. To slowly toil through each step of the process and eventually reap your rewards is a delight, and even if the combat encounters can sometimes become a frustrating chore, the slow process of cultivating the rice harvest is always a treat.
Sakuna: Of Rice and Ruin is a wonderful mix of the two ideas. As a platformer, the game wouldn’t have enough driving force, and would wear out quickly. For farming, while it’s truly lovely, there’s too much downtime with not enough to do. Each of these things in a game of their own would be draining, but together it creates a Reese’s Peanut Butter Cup of a game that deserves recognition and continues to feel fresh and enjoyable even after 20 hours in.
This is a truly impressive 2.5D action platformer that boasts some of the best production values on the entire Switch’s library, with gorgeous visuals and a great soundtrack. Its gameplay is fast-paced and addictive, and its slice of life mechanics, while far from being the best thing about it, are still interesting and not very intrusive.
Sakuna: Of Rice and Ruin is a heavenly combination of realistic farming, combat and exploration served with a hearty side of great characters and writing
Sakuna: Of Rice and Ruin may falter in some areas, but the action-platforming is some really well-done stuff, with fun and complex combat, great level design, and actually challenging boss fights.