Cyberpunk 2077’s Path Tracing Replace – Chips and Cheese

2023-05-07 14:30:18

{Hardware} raytracing acceleration has come a good distance since Nvidia’s Turing first launched the know-how. Even with these {hardware} advances, raytracing is so costly that the majority video games have caught to very restricted raytracing results, if raytracing is used in any respect. Nonetheless, some know-how demos have offered expanded raytracing results. For instance, Quake and Portal have been given path tracing modes the place every little thing is rendered with raytracing.

Cyberpunk 2077 is the latest sport to get a full path tracing mode, in a recent patch that calls it “Overdrive”. Not like Quake and Portal, Cyberpunk is a really current and demanding AAA sport that requires numerous {hardware} energy even with out the trail tracing know-how demo enabled. We beforehand analyzed Cyberpunk 2077 together with a few different video games, however that was earlier than the trail tracing patch.

Path Tracing

Identical to with the earlier article, we’re taking a profile whereas wanting down Jig Jig avenue. With path tracing enabled, the RX 6900 XT struggles alongside at 5.5 FPS, or 182 ms per body. Body time is unsurprisingly dominated by an enormous 162 ms raytracing name. Curiously, there’s nonetheless a little bit of rasterization and compute shaders originally of the body. Raytracing calls that correspond to the non-path tracing mode appear to stay as nicely, however all of that’s shoved into insignificance by the large raytracing name added by path tracing mode.

RGP solely collects metrics for the primary shader engine. Sadly, that shader engine finishes its work early, so we don’t get counters for a big a part of the body.

This large raytracing name generates rays in a 1920×1080 grid, or one invocation per pixel. Wanting deeper, the large raytracing name suffers from poor occupancy. RDNA 2’s SIMDs can monitor 16 wavefronts, making it a bit like 16-way SMT on a CPU. Nonetheless, it’s restricted to 4 wavefronts on this case as a result of every thread makes use of the utmost allocation of 256 registers per thread. Not like CPUs, which reserve a set variety of ISA registers for every SMT thread, GPUs can flexibly allocate registers. But when every thread makes use of too many registers, it’ll run out of register file capability and never be capable to monitor as many threads. Technically every thread solely asks for 253 registers, however RDNA allocates registers in teams of 16, so it behaves as if 256 registers had been used.

Low occupancy means the SIMDs have issue discovering different work to feed the execution models whereas ready on reminiscence accesses. Radeon GPU Profiler demonstrates this by exhibiting how a lot of instruction latency was “hidden” as a result of the SIMD’s scheduler was capable of difficulty different directions whereas ready for that one to complete.

s_waitcnt vmcnt(n) waits for pending reminiscence accesses to finish. On this case, 553 clocks of reminiscence latency had been partially hidden by scalar ALU and vector ALU (inexperienced and dashed yellow portion), however most of it resulted in stalls

To make certain, GPU cache latency is totally brutal and execution models will virtually positively face stalls even when reminiscence entry requests are glad from cache. However greater occupancy will normally let a GPU cover extra cache latency by discovering different work to do whereas ready for information to return again.

Montage of information obtainable from the RX 6900XT by way of RGP, with counters restricted to the portion when SE 0 was doing stuff

Regardless of low occupancy, Cyberpunk 2077’s path tracing name achieves respectable {hardware} utilization. Loads of that’s because of respectable cache hitrates. Roughly 96.54% of reminiscence accesses are served from L2 or sooner caches, retaining them off the upper latency Infinity Cache and VRAM. Instruction and scalar cache hitrates are glorious. Nonetheless, there’s room for enchancment. L0 cache hitrate is mediocre at simply 65.91%. The L1 has very poor hitrate. It does convey cumulative L0 and L1 hitrate to 79.4%, however there’s a really huge soar in latency between L1 and L2.

Lengthy-Tailed Occupancy Conduct

GPUs profit from numerous thread-level parallelism, however a activity with numerous threads doesn’t end till all the threads end. Subsequently, you need work evenly distributed throughout your processing cores, in order that they end at about the identical time. Normally, GPUs try this moderately nicely. Nonetheless, one shader engine completed its work after 70 ms, leaving 1 / 4 of the GPU idle for the following 91 ms.

I’m wondering if RT work is rather a lot much less predictable than rasterization workloads, making workload distribution more durable. For instance, some rays may hit a matte, opaque floor and terminate early. If one shader engine casts a batch of rays that every one terminate early, it might find yourself with rather a lot much less work even when it’s given the identical variety of rays to begin with.

RDNA 3 Enhancements, and Raytracing Modes In contrast

RDNA 2 has been outmoded by AMD’s RDNA 3 structure, which dramatically improves raytracing efficiency. I attribute this to a number of components. The vector register file and caches acquired notable capability will increase, attacking the reminiscence entry latency downside from two instructions (higher occupancy and higher cache hitrates). New traversal stack administration directions scale back vector ALU load. Twin difficulty functionality additional improves raytracing efficiency by getting by means of non-raytracing directions sooner. Most of those {hardware} modifications, apart from the brand new traversal stack administration directions, ought to enhance efficiency for non-raytracing workloads too.

Related stats for RDNA 3, because of Titanic. Nonetheless, a unique scene was captured as a result of the Jig Jig scene did not seize.

RDNA 3’s SIMDs have a 192 KB vector register file, in comparison with 128 KB on RDNA 2. That doubtlessly lets RDNA 3 maintain extra waves in flight, particularly if every shader desires to make use of numerous registers. Raytracing kernels apparently use numerous vector registers, so RDNA 3 beneficial properties a transparent occupancy benefit. Cyberpunk 2077’s path tracing name is very hungry for vector registers. For comparability, the traditional raytracing mode makes use of far fewer registers and enjoys greater occupancy. It’s additionally invoked over a grid with half of the screens vertical decision, in all probability to make the load lighter.

Situation	Name	Vector Register (VGPR) Utilization	Occupancy (Per SIMD)
RDNA 2, Path Tracing	DispatchRays<Unified>(1920, 1080, 1)	253 used, 256 allotted 32 KB per wave	4 waves
RDNA 2, RT Extremely	DispatchRays<Unified>(960, 1080, 1)	101 used, 112 allotted 14 KB per wave	9 waves
RDNA 3, Path Tracing	DispatchRays<Unified>(1920, 1080, 1)	254 used, 264 allotted 33.7 KB per wave	5 waves
RDNA 3, RT Psycho	DispatchRays<Unified>(960, 1080, 1)	99 used, 120 allotted 15.3 KB per wave	12 waves

Alongside a much bigger vector register file, RDNA 3 dramatically will increase cache sizes. L0 capability goes from 16 KB to 32 KB, L1 goes from 128 KB to 256 KB, and L2 goes from 4 MB to six MB. The result’s that 83.2% of requests are dealt with from L1 or sooner caches. Cumulative L2 hitrate doesn’t see a notable enchancment. However servicing extra requests from inside a shader engine means common reminiscence entry latency goes down. Mix that with greater occupancy, and RDNA 3 makes higher use of bandwidth.

Settings not precisely matched for normal raytracing. Totally different individuals at CnC personal totally different playing cards and communication is difficult. Additionally RDNA 3 scalar cache counters seem like damaged

Common raytracing additionally enjoys higher {hardware} utilization, throughout each GPUs. That’s as a result of it will get greater occupancy within the first place, regardless that its cache hitrates and instruction combine is essentially related. With common raytracing, {hardware} utilization on RDNA 2 goes from mediocre to a fairly good degree.

Occupancy is a key think about higher {hardware} utilization, since different traits stay related throughout the 2 raytracing workloads. For instance, cache hitrates are fairly comparable.

	RDNA 2, Path Tracing	RDNA 2, RT Extremely, Longest Length RT Name
L0 Hitrate	65.9%	55.53%
L1 Hitrate	41.96%	43.83%
L2 Hitrate	83.27%	84.47%
Instruction Cache Hitrate	99.54%	90.65%
Scalar Cache Hitrate	96.41%	99.08%

RDNA 3 additionally makes ISA enhancements to enhance RT core utilization. New LDS (native information share) directions assist raytracing code handle their traversal stack extra effectively. A raytracing acceleration construction is a tree, and traversing a tree entails retaining monitor of nodes it’s a must to return to, with the intention to go to their different youngsters.

The native information share is a quick, per-WGP scratchpad reminiscence that gives constantly low latency and excessive bandwidth, making it a perfect place to retailer the traversal stack. RDNA 2 makes use of generic LDS learn and write directions, with a batch of different directions to handle the stack. RDNA 3 introduces a brand new LDS instruction that automates traversal stack administration.

A couple of primary blocks that seem like doing traversal stack administration on RDNA 2 and RDNA 3

In consequence, RDNA 3 can deal with bookkeeping duties with fewer directions. Hit rely for LDS directions is almost halved on RDNA 3 in comparison with RDNA 2. Wanting additional at instruction hit counts, RDNA 3 additionally sees fewer branches in comparison with RDNA 2. For the reason that ds_bvh_rtn_stack_b32 instruction takes the tackle of the final node visited and determines the following node to go to, it presumably avoids branches that we see in RDNA 2 code.

All of those enhancements come collectively to let RDNA 3 make much better use of its intersection take a look at {hardware}. RDNA 2 was capped at 1.9 GHz right here for constant clocks whereas RDNA 3 was not. However RDNA 3 is doing intersection assessments at a far greater fee than what clock speeds and elevated WGP rely would account for.

Nvidia’s RTX 4070

Nvidia’s previous few generations of video playing cards have closely emphasised raytracing. Ada Lovelace, the structure behind Nvidia’s 4000 collection playing cards, is not any exception. Profiling the identical Jig Jig avenue scene reveals that the very lengthy period path tracing name takes 26.37 ms, which is a wonderful efficiency. For comparability, the RX 7900 XTX took 34.2 ms, although on a unique scene. The 6900 XTs is off the charts (within the unsuitable path), taking on 160 ms. A part of that’s as a result of I ran the cardboard at a locked 1.9 GHz, however rising clocks by 10-20% would nonetheless put RDNA 2’s efficiency far decrease than that of RDNA 3 or Ada.

Occupancy on Nvidia’s RTX 4070 all through the body period. Grey = unused, mild grey = idle, yellow = compute, blue = vertex/tesselation management/geometry shaders, inexperienced = pixel shaders

Ada Lovelace’s basic constructing block is the SM, or streaming multiprocessor. It’s roughly analogous to RDNA’s WGP, or workgroup processor. Like a RDNA 2 WGP, an Ada SM has 128 FP32 lanes, cut up into 4 partitions. Nonetheless, every partition only has a 64 KB vector register file, in comparison with 128 KB on RDNA 2. Most occupancy can be decrease, at 12 waves per partition in comparison with 16 on RDNA 2. For the trail tracing name, Nvidia’s compiler opted to allocate 124 vector registers, giving the identical occupancy as RDNA 2. Every partition solely tracks 4 waves (or warps, in Nvidia’s terminology), principally making a SM or WGP run in SMT-16 mode. I imagine Nvidia’s ISA solely permits addressing 128 registers, whereas AMD’s ISA can tackle 256 registers. So, each Nvidia and AMD’s architectures are almost maxing out their per-thread register allocations.

Nsight makes an attempt to be as complicated as doable. Dividing registers per SM by 16 warps per SM provides 3964 registers per warp. Dividing once more by 32 threads per warp provides 124 registers per thread.

{Hardware} utilization seems just like that of RDNA 3, although a direct comparability is troublesome due to architectural variations and totally different metrics reported by AMD and Nvidia’s profiling instruments. Ada’s SM is nominally able to issuing 4 directions per cycle, or one per cycle for every of its 4 partitions. The SM averaged 1.7 IPC, reaching 42.7% of its theoretical instruction throughput.

Whereas AMD presents one VALU, or vector ALU, utilization metric, Nvidia has 4 metrics comparable to totally different execution models. Based on their profiling guide, FMA Heavy and Gentle pipelines each deal with primary FP32 operations. Nonetheless, the Heavy pipe may also do integer dot merchandise. The profiling information additionally states that the ALU pipe handles integer and bitwise directions. Load seems to be nicely distributed throughout the pipes.

Sadly Nsight doesn’t present instruction-level information like AMD’s Radeon GPU Profiler. Nonetheless, it does present commentary on how branches have an effect on efficiency. RDNA and Ada each use 32-wide waves, which suggests one instruction applies throughout 32 lanes. That simplifies {hardware}, as a result of it solely has to trace one instruction pointer as a substitute of 32.

Illustrating how divergence reduces execution unit utilization with a easy analogy. If a subset of lanes take the department, appropriate operation is achieved by masking off some lanes, stopping the cake from being a lie

It additionally creates an issue if it hits a conditional department, and the situation is barely true for a subset of these 32 lanes. GPUs deal with this “divergence” scenario by disabling the lanes for which the situation holds true. Within the path tracing name, on common solely 11.6 threads had been lively per 32-wide warp. Loads of throughput is due to this fact left on the desk.

AMD virtually definitely faces the same downside, because it makes use of related 32-wide waves. Nonetheless, Radeon GPU Profiler doesn’t present stats to substantiate this.

On the reminiscence entry entrance, a direct comparability is similiarly infeasible. RDNA has separate paths to its LDS (equal to Nvidia’s Shared Reminiscence) and first degree information caches. Ada, like many prior Nvidia architectures, can flexibly allocate L1 and Shared Reminiscence capability out of a single block of SRAM, by various how a lot is tagged to be used as a cache. LDS or Shared Reminiscence utilization is similiar in comparison with RDNA 2 (2% for Ada, 3% for RDNA 2), however a lot decrease than the 11.6% for RDNA 3. I think RDNA 3’s specialised LDS traversal stack administration directions shove numerous work to the LDS, and Radeon GPU Profiler stories greater utilization because of this.

From the L1 cache aspect, utilization is in a very good place at 20-30%. Most reminiscence accesses are information entry slightly than texture ones, which is sensible for a raytracing kernel. L1 hitrate is 67.6%, which surprisingly isn’t pretty much as good as I assumed it will be. AMD’s RDNA 3 achieves 73.39% L0 hitrate, whereas RDNA 2 will get 65.9%. Maybe Ada needed to allocate numerous L1 capability to be used as Shared Reminiscence, which means it couldn’t use as a lot for caching.

As soon as we get to L2, every little thing appears to be like rather a lot higher for Ada. In a earlier article, we famous how Ada’s benefits from excellent caching. Nvidia sticks to 2 ranges of cache, whereas AMD makes use of 4 ranges, and due to this fact enjoys decrease latency accesses to Infinity Cache sized reminiscence footprints. The RTX 4070’s L2 cache is considerably smaller than that of flagship merchandise just like the 4090, however it nonetheless performs very nicely for Cyberpunk’s path tracing workload. Hitrate is a wonderful 97%. L1 already caught 67.6% of reminiscence accesses, so cumulative hitrate to L2 is over 99%.

L2 throughput utilization is 41.9%, which can be a very good place to be. Utilization above 70-80% would suggest a bandwidth restricted situation, which might positively be a priority as a result of Ada’s L2 cache has related capability to AMD’s Infinity Cache, however has to deal with extra bandwidth. Fortunately, Nvidia’s L2 cache could be very a lot as much as the duty. Ada due to this fact does a superb job of retaining its execution models fed with a much less complicated cache hierarchy.

Video Reminiscence Utilization

Video reminiscence capability limitations have a nasty behavior of degrading video card efficiency in fashionable video games, lengthy earlier than their compute energy turns into insufficient. In the present day, it’s particularly a difficulty as a result of there are many very highly effective midrange playing cards outfitted with 8 GB of VRAM. For perspective, AMD had 8 GB playing cards obtainable in late 2015 with the R9 390, and Nvidia did the identical in 2016 with the GTX 1080.

Visualization of VRAM allocations from Radeon Reminiscence Visualizer, for CP2077’s path tracing mode

With the trail tracing “overdrive” mode lively, Cyberpunk 2077 allocates 7.1 GB of VRAM. Multi-use buffers are the biggest VRAM client, consuming 4.91 GB. Textures come subsequent, with 2.81 GB. Raytracing-specific reminiscence allocations aren’t too heavy at barely over 400 MB. Path tracing mode does allocate a bit extra VRAM than the common raytracing mode, and most of that’s right down to multi-use buffers. These buffers are in all probability getting used to assist raytracing, even when Radeon Reminiscence Visualizer doesn’t have sufficient information to straight attribute them to RT.

“Different” contains descriptors, command buffers, and reminiscence allotted for inner utilization.

Whereas this degree of VRAM utilization ought to be high-quality for enjoying the sport in isolation, PCs are sometimes used to multitask (not like consoles). A person might simply have a sport walkthrough information, Discord, and recording software program open within the background. They could even be enjoying a video whereas gaming. All of these take video reminiscence, and will find yourself squeezing a 8 GB card.

Reminiscence allocation visualization for the traditional RT mode, with extremely lighting

Fortunately, the scenario is much less tight with the common raytracing mode. With 6.66 GB of reminiscence allotted and certain, there ought to be an honest quantity of VRAM left to permit background functions to operate correctly, or to deal with greater decision texture mods.

Upscaling Applied sciences

Even restricted raytracing results are nonetheless too costly for the overwhelming majority of video playing cards in circulation. That’s the place upscaling applied sciences are available. The picture is rendered at a decrease decision, after which scaled as much as native decision, hopefully with out noticeable artifacts or picture high quality degradation. Together with path tracing, CD Projekt Pink has added support for Intel’s XeSS upscaler. After all, FSR assist stays in place, however the two upscalers can’t be stacked.

Timelines roughly scaled to match. Potential upscaling computation part marked by pink bracket

Two of the early raytracing calls are dispatched with grid sizes equal to the display decision, whereas the longer period ones are run with half of the horizontal decision (giving one invocation per two pixels). Wanting these calls provides us a good suggestion of native rendering decision.

XeSS Setting	Native Decision?	Body Time Earlier than Upscaling Part?	Upscaling Computation Length?	Framerate
None	1920×1080	29.7 ms	N/A	32.7 FPS
Extremely High quality	1477×831	21.8 ms	1.309 ms	42.2 FPS
High quality	1280×720	18.6 ms	1.292 ms	46.6 FPS
Balanced	1130×636	17.2 ms	1.286 ms	52.6 FPS
Efficiency	960×540	14.5 ms	1.273 ms	61.2 FPS

Operating with non-overdrive RT mode with lighting set to extremely. FPS is predicated off period of the profiled body. Bear in mind the GPU is capped at 1.9 GHz for constant clocks. Inventory settings will lead to greater framerates

A few of XeSS’s shaders use tons of dot merchandise. Intel says XeSS uses a neural network, we’re in all probability seeing matrix multiplications used for AI inferencing. v_dot4_i32_i8 directions deal with two 32-bit supply registers as vectors of 4 INT8 components, computes the dot product, after which provides that with a 32-bit worth. AI inferencing usually makes use of decrease precision information varieties, and use of INT8 exhibits a deal with velocity.

Do you want dot merchandise? In that case, you’ll like XeSS

Nonetheless, the longer working XeSS kernels, like one which runs for 0.245 ms, don’t do dot merchandise. As a substitute, there’s a mixture of common FP32 math directions, conversions between FP32 and INT32, and texture masses. Machine studying just isn’t as easy as making use of a batch of matrix multiplications. Knowledge needs to be formatted and ready earlier than it may be fed right into a mannequin for inference, and I think we’re seeing a little bit of that.

Snippet from the 0.245 ms Dispatch(120,34,1) name, exhibiting some excessive latency texture/reminiscence accesses

XeSS’s calculations happen over about 16 calls, all of which used wave64 mode. Cache hitrates had been 70.22%, 7.55%, and 60.84% for L0, L1, and L2 respectively in the course of the XeSS part. Principally, if a request missed L0, it in all probability wasn’t going to hit L1. Instruction cache hitrate was solely 80.86%, indicating that instruction footprints could also be massive sufficient to spill out of L1i. Scalar cache hitrate was respectable at 95.85%.

{Hardware} utilization was affordable regardless that occupancy was usually not a brilliant spot. For those who largely have straight line code stuffed with math directions, you don’t want numerous wavefronts in flight to cover reminiscence entry latency.

Spot checking a couple of of the compute shaders utilized by XeSS. Stats are from XeSS run on the Extremely High quality preset

A number of the Dispatch() calls are extraordinarily small. For instance, name 5495 solely launched 48 wavefronts. The 6900 XT has 160 SIMDs, so numerous them will likely be sitting idle with no work to do. Synchronization limitations stop the GPU from overlapping different work to raised fill the shader array, so XeSS has a little bit of hassle totally using a GPU as huge because the 6900 XT.

After the XeSS part has completed, no less than one subsequent DrawInstanced name creates 2,073,601 pixel shader invocations, which corresponds to 1 thread per pixel for 1920×1080. Even with upscaling in use, some draw calls seem to render at full decision. These might need to do with UI components, which take little or no time to render no matter decision, and have extra to lose than achieve from upscaling. The identical sample applies to FSR.

In comparison with FSR

FSR, or FidelityFX Tremendous decision, is AMD’s upscaling know-how. FSR got here out nicely earlier than XeSS, and was already obtainable in Cyberpunk 2077 earlier than the “Overdrive” patch. Not like XeSS and Nvidia’s DLSS, FSR doesn’t use machine studying. That additionally means it doesn’t require particular {hardware} options like DP4A or matrix multiplication acceleration to carry out nicely. It’s due to this fact way more extensively relevant than Intel or Nvidia’s upsamplers, and can be utilized on very outdated {hardware}.

FSR Setting	Native Decision?	Body Time Earlier than Upscaling Part?	Upscaling Computation Length?	Framerate
None	1920×1080	29.7 ms	N/A	32.7 FPS
High quality	1280×720	17.7 ms	0.576 ms	53 FPS
Balanced	1129×635	15.7 ms	0.534 ms	59.76 FPS
Efficiency	960×650	13.7 ms	0.52 ms	67.5 FPS
Extremely Efficiency	640×360	10.8 ms	0.478 ms	85.2 FPS

In comparison with XeSS, FSR begins off at decrease native resolutions and spends much less time doing upscaling computations. That leads to greater framerates at equally labeled high quality settings. Like XeSS, FSR makes use of a set of compute shaders that run in wave64 mode, with synchronization limitations between them. FSR makes much more environment friendly utilization of compute assets, because of greater occupancy and higher cache hitrates. In some instances, FSR sees such good {hardware} utilization that it is likely to be a bit compute certain.

Checking occupancy and {hardware} utilization for FSR’s high quality preset

Instruction footprint is decrease than with XeSS, bringing instruction cache hitrates again as much as very excessive ranges. RDNA 2’s L1 cache does higher, however nonetheless sees extra hits and misses. As with earlier than, the L0 and L2 caches catch the overwhelming majority of accesses. FSR’s reminiscence entry patterns are extra cache-friendly than XeSS’s in an absolute sense, and that contributes to feeding the execution models.

Not like XeSS, FSR’s directions appear to be a broad mixture of vector 32-bit operations with loads of picture reminiscence (texture) directions combined in. Math directions largely take care of FP32 or INT32, not like XeSS’s use of decrease precision with dot merchandise.

Snippets from the longest period FSR compute kernel

I additionally really feel like straight evaluating FSR to XeSS is a bit doubtful. The 2 upscalers take totally different approaches and have totally different use instances, regardless that there’s positively overlap. XeSS employs costlier upscaling methods like machine studying, and tends to render at greater native resolutions to begin. It’s nicely suited to fashionable, excessive finish graphics playing cards the place a reasonable framerate bump is sufficient to go from a barely low framerate to a playable one. FSR alternatively begins with decrease native resolutions. It makes use of a brutally quick and environment friendly upscaling go that doesn’t depend on specialised operations solely obtainable on newer GPUs. That makes it higher suited to older or smaller GPUs, the place it may possibly ship a bigger framerate improve.

Upscaling on Zen 4’s iGPU

One instance of a small GPU is Zen 4’s built-in graphics implementation. As a result of it’s not meant to deal with heavy gaming duties, it has a single RDNA 2 WGP with 128 FP32 lanes. Caches are tiny as nicely, with a 64 KB L1 and 256 KB L2. That is the smallest RDNA 2 implementation I’m conscious of. I’m testing FSR with raytracing off as a result of efficiency is already extraordinarily low with out raytracing.

Upscaler	Body Time earlier than Upscaling Part	Upscaling Computation Length?	Framerate
XeSS High quality	157.9 ms	21.2 ms	5.4 FPS
FSR High quality	137.9 ms	11.7 ms	6.3 FPS

Framerate is low and leaving many of the rasterization settings at excessive or extremely was perhaps not the very best thought

XeSS now takes an additional 10 ms to do its upscaling. Zen 4’s iGPU is extraordinarily compute certain when dealing with XeSS’s dot product kernels. That highlights a major weak point of XeSS. Time spent upscaling doesn’t change a lot whatever the high quality setting, so XeSS supplies virtually all of its efficiency enhance by permitting the body to be rendered at decrease decision. On a small GPU, the upscaling time might find yourself being a good portion of body time, placing a low cap on framerate. That makes XeSS much less appropriate for tiny GPUs, which want upscaling probably the most.

Checking {hardware} utilization in a few of XeSS’s compute kernels on Zen 4’s iGPU

One other approach of taking a look at it’s that XeSS makes glorious use of the iGPU’s compute assets. Earlier, we noticed some tiny dispatches that weren’t sufficiently big to fill the 6900 XT’s shader array. With one WGP as a substitute of 40, that’s not an issue.

FSR can be extraordinarily compute certain, however has much less work to do within the first place. In each instances, we see that filling a small iGPU is rather a lot simpler than doing the identical with a big discrete GPU, regardless that the discrete GPU has a a lot stronger cache hierarchy.

Checking {hardware} utilization for FSR’s kernels

Regardless that XeSS and FSR’s “high quality” settings seem to make use of the identical native rendering decision, XeSS spends longer rendering the native body. One perpetrator is a really lengthy compute shader towards the center of the body. The shader seems to be executing an identical code in each instances, however the XeSS body invoked 28,960 wavefronts (926,720 threads), in comparison with 28,160 wavefronts (901,120 threads) on the FSR body. Maybe there’s extra to XeSS and FSR’s variations than simply the upscaling go and native decision. In any case, we’d want the next efficiency preset to run excessive settings even with out raytracing.

Occupancy timeline for FSR and XeSS, with out raytracing and most settings on excessive or extremely. Time scales not matched to indicate upscaling value relative to border period. Yellow = Compute, Blue = Pixel Shaders, Inexperienced = Vertex Shaders

Zooming again out, Cyberpunk 2077’s rendering workload is dominated by compute shaders even with raytracing disabled. Vertex and pixel shaders solely account for a minority of body time. Distinction that with indie and older video games, the place pixel shaders dominate.

Path Tracing on Zen 4’s iGPU?

Cyberpunk 2077 explicitly recommends a RTX 4090 or 3090 for the trail tracing know-how demo. At first look, this iGPU might not essentially meet the exact definition put forth by that advice. Nonetheless, after a little bit of considering you could conclude that one WGP just isn’t that a lot smaller than Van Gogh’s iGPU, which is barely a bit smaller than the RX 6500XT, which is barely a bit smaller than the RX 6700XT, which is barely a bit smaller than the RX 6900 XT, which is barely a bit smaller than the RX 7900 XTX, which is barely a bit smaller than the RTX 4090. As a result of Zen 4’s built-in GPU clearly virtually meets the beneficial specs, let’s see the way it does.

With out FSR, AMD’s profiling instruments had been unable to take a body seize in any respect, regardless that the GPU was capable of render a body each few seconds. With FSR’s extremely efficiency mode, Cyberpunk 2077 runs at 4.1 FPS wanting down Jig Jig avenue. The expertise is unquestionably extra cinematic than playable. Profiling a body was troublesome too, as a result of the capturing course of appears to expire of buffer house and drops occasions in direction of the tip of the body. Radeon GPU Profiler’s occupancy timeline solely exhibits 237 out of 243 ms, dropping some information from the tip.

Occupancy timeline for CP2077’s path tracing demo working on Zen 4’s iGPU. Profile was not capable of seize all the body period

Like earlier than, we see a really lengthy period raytracing name with a little bit of long-tailed conduct. Nonetheless, the long-tailed conduct is nowhere close to as unhealthy because it was with the 6900 XT. In spite of everything, it’s unimaginable for one shader engine to complete its work early and sit idle ready for others to complete, in case you solely have one shader engine to begin with.

Identical to on larger GPUs, body time is dominated by an enormous raytracing name. Nonetheless, this time it’s a DispatchRays<Oblique> name, not a DispatchRays<Unified> one. The unified DispatchRays name appears to do every little thing in a single massive shader with out subroutine calls. In distinction, the oblique one makes use of totally different capabilities for ray technology, traversal, and hit/miss dealing with.

From this, we are able to see that many of the raytracing value comes from ray traversal, with ray technology additionally taking on a major quantity of labor. This oblique variation of DispatchRays makes use of half as many vector registers (128) because the unified path shading kernel from the start of the article. That enables every SIMD to trace eight waves, as a substitute of simply 4.

Instance of calls from a ray traversal shader to hit shaders

I’m undecided which method is best. Latency is less complicated to cover when you’ve got extra waves in flight. However operate calls incur overhead too. The closest hit/any hit shaders have prolonged prologue and epilogues. s_swappc_b64 and s_setpc_b64 directions are used to name into and return from capabilities respectively, and are principally oblique branches. I can’t think about them being low cost both.

{Hardware} utilization as reported by RGP

In the long run, each approaches obtain related {hardware} utilization. The instruction combine can be related, with roughly one in each hundred executed directions being a raytracing one. Vector ALU directions account for about half the executed directions, whereas scalar ALU account for just below 1 / 4 of the instruction combine. Simply above 10% are branches, so we’re once more seeing that raytracing is branchier than different shaders.

FSR’s extremely efficiency preset is clearly not sufficient to offer a premium expertise on Zen 4’s iGPU.

Nonetheless, seeing all of the lighting results from Cyberpunk 2077’s know-how demo rendered on an iGPU remains to be wonderful, even when pixel-level high quality is closely compromised by upscaling.

Closing Phrases

Raytracing is an costly however promising rendering approach. Expertise demos like Cyberpunk’s present that raytracing has numerous potential to ship reasonable lighting. Nonetheless, in addition they present that we’re nonetheless far-off from having sufficient GPU energy to profit from raytracing’s potential. Analyzing Cyberpunk 2077’s Overdrive patch exhibits that raytracing each requires extra work, and more durable work. Raytracing sections have extra branches and decrease occupancy, which means GPUs face challenges in retaining their execution models fed.

With that in thoughts, Nvidia’s characterization of raytracing because the “holy grail” of sport graphics is a bit inaccurate. The present state of raytracing in video games just isn’t the holy grail, as there’s loads of untapped potential. For those who’ve used Blender earlier than, you’ll know which you could improve high quality by permitting extra ray bounces or getting extra samples per pixel (rising ray rely). Moreover, Cyberpunk 2077’s path tracing mode is so heavy that it has to lean on upscaling applied sciences to ship a playable expertise.

Cyberpunk’s patch additionally provides XeSS, which brings our focus to upscaling applied sciences. FSR and XeSS are the other of raytracing. As a substitute of slicing into efficiency to ship extra spectacular visuals, upscaling seeks to enhance framerate with minimal loss to visible high quality. Not like raytracing calls, upscaling requires little or no work. On prime of that, the upscaling go runs very effectively on GPUs. Branches are virtually absent. As a substitute, upscaling makes use of loads of straight line code dominated by vector ALU directions. GPUs love that. Nvidia additionally has an upscaling know-how referred to as DLSS, however we didn’t profile it as a result of Nsight was a ache to get working within the first place for the one hint of Cyberpunk 2077 that we had been capable of get for this text.

As for upscaling, it’s right here to remain for the forseeable future. Actual time raytracing is drastically inefficient in comparison with rasterization. A number of the latest excessive finish playing cards can use little or no upscaling or no upscaling in any respect, and nonetheless ship raytraced results at playable framerates. However GPU costs are ridiculous lately. A RTX 4090 sells for over $1600, whereas the 7900 XTX sells for round $1000. Distinction that with the Pascal technology, the place a GTX 1080 offered for $600, and the 1080 Ti went for $700. Excessive finish playing cards are going to be too costly for lots of players, making upscaling extraordinarily essential going ahead. Hopefully, advances in each GPU energy and upscaling applied sciences will give us higher raytraced results with very technology.

As a ultimate observe, we did attempt to get information from an A750 as nicely. Nonetheless, we couldn’t get a body seize utilizing Intel’s Graphics Efficiency Analyzer.

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