CUDA

as donto believed on

From Dontopedia, the open, paraconsistent wiki. (Last updated 2026-06-10.)

CUDA has 59 facts recorded in Dontopedia across 36 references, with 2 live disagreements.

59 facts·31 predicates·36 sources·2 in dispute

Mostly:rdf:type(18), has function(2), preferred over rocm(1)

Rdf:typein dispute`rdf:type`

Technology^[15]all time · 33
Software Platform^[16]all time · 44
Software Platform^[18]all time · 31
Technology^[19]all time · 68
Software Platform^[20]all time · 9
Compute Platform^[21]all time · 266
Platform^[23]all time · 473
Device Type^[24]all time · 5a00c51f Dd1e 428b B79b 370b9163f60f
Gpu Accelerator^[25]all time · C6ee25c2 5292 4256 95f3 8b4c1563623a
Device^[26]all time · E04766e0 B70f 4cd4 93df 3375bb36ef45

Inbound mentions (49)

Other subjects in dontopedia point AT this entity as a value. These are inverse relationships — e.g. "X motherOf this subject" — and answer questions the forward facts can't. Grouped by predicate.

`isDeployedOn`Is Deployed on(3)

Inputsex:inputs
Targetsex:targets
Pytorch Modelpytorch-model

`fasterImplementation`Faster Implementation(2)

Row 1024ex:row-1024
Row 2048ex:row-2048

`hasModule`Has Module(2)

Torchex:torch
Torchex:torch

`referencesHardware`References Hardware(2)

Textex:text
Textex:text

`abstracts`Abstracts(1)

Gpubackend Traitex:gpubackend-trait

`aimsToBeat`Aims to Beat(1)

Custom Vulcan Codeex:custom-vulcan-code

`beingFiguredOut`Being Figured Out(1)

Cudaex:cuda

`canBe`Can Be(1)

Deviceex:device

`checksAvailability`Checks Availability(1)

Device Handlingex:device-handling

`comparedTo`Compared to(1)

Wgpuex:wgpu

`compares`Compares(1)

Xenonfunex:xenonfun

`comparesTo`Compares to(1)

Ajaxdavis Implementationex:ajaxdavis-implementation

`compatibleWithoutChanges`Compatible Without Changes(1)

Resonate Homeex:resonate-home

`competesWith`Competes With(1)

Heliosex:helios

`configuredFor`Configured for(1)

Python Baseex:python-base

`contrastsWith`Contrasts With(1)

Mlxex:mlx

`didntWantToRun`Didnt Want to Run(1)

Diaex:dia

`enabledBy`Enabled by(1)

Move Model and Data to Gpuex:move-model-and-data-to-gpu

`enablesDispatchFor`Enables Dispatch for(1)

Cuda Dispatch Tierex:cuda-dispatch-tier

`hasLoser`Has Loser(1)

Benchmark Result 1ex:benchmark-result-1

`hasPoorGPUParallelism`Has Poor Gpu Parallelism(1)

Torch Cumsumex:torch-cumsum

`hasSupportFor`Has Support for(1)

Mlxex:mlx

`implicatesBetterPerformance`Implicates Better Performance(1)

Higher Throughputex:higher-throughput

`involvesFramework`Involves Framework(1)

Direction Mlx Faithfulex:direction-mlx-faithful

`isMovedTo`Is Moved to(1)

Input Tensorex:input_tensor

`isMovedToDevice`Is Moved to Device(1)

Input Tensorex:input_tensor

`isOptimizedFor`Is Optimized for(1)

Fused Cuda Selective Scan Kernelex:fused-cuda-selective-scan-kernel

`isReadyFor`Is Ready for(1)

Compute Rs Dispatch Layerex:compute-rs-dispatch-layer

`isSpecificTo`Is Specific to(1)

Cuda Dispatch Tierex:cuda-dispatch-tier

`needsDecouplingForCuda`Needs Decoupling for Cuda(1)

Symbiogenesis Featureex:symbiogenesis-feature

`originallyTargets`Originally Targets(1)

Python Baseex:python-base

`parallelismWinsOn`Parallelism Wins on(1)

Fft Trickex:fft-trick

`plansToCompareBenchmark`Plans to Compare Benchmark(1)

Ajaxdavisex:ajaxdavis

`playsWith`Plays With(1)

Girvoex:girvo

`preferredDevice`Preferred Device(1)

Cuda or Cpuex:cuda-or-cpu

`presupposesExistenceOf`Presupposes Existence of(1)

Textex:text

`requiresDecoupling`Requires Decoupling(1)

Symbiogenesis Featureex:symbiogenesis-feature

`shouldBeUsedFromStart`Should Be Used From Start(1)

Torch Cumsum Exponential Decayex:torch-cumsum-exponential-decay

`suggestsAvoiding`Suggests Avoiding(1)

Ajaxdavisex:ajaxdavis

`targets`Targets(1)

Cuda Empty Cacheex:cuda-empty-cache

`targetsDevice`Targets Device(1)

Optimized Codeex:optimized-code

`usesCInsteadOf`Uses C Instead of(1)

Training Harness Typescript C Gpuex:training-harness-typescript-c-gpu

`usesCuda`Uses Cuda(1)

Dgx Sparksex:dgx-sparks

`warnsAboutTweak`Warns About Tweak(1)

Xenonfunex:xenonfun

Other facts (31)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

31 facts

Predicate	Value	Ref
Has Function	Empty Cache	[32]
Has Function	Empty Cache	[34]
Preferred Over Rocm	Roc M	[1]
Avoids Rocm Pain	Roc M	[1]
Faster Than	Helios	[2]
Serves As Baseline	Helios Benchmark	[2]
Resumes From	Checkpoint	[3]
Nvidia Context	Mps Vs Cuda	[4]
Benefits From Fft Parallelism	Torch Cumsum Poor	[5]
References Nvidia Compute Platform	null	[6]
Uses Cudarc Crate	Cudarc Crate	[7]
Superior in Grid Support	Metal Kernel	[7]
Has Native2d3d Grid Support	Native 2d 3d Grid Support	[7]
Has Specific Wins	Shared Memory Halo Loading	[7]
Historical Alternative to Metal	Metal Gpu	[7]
Compared Superior to	Metal Kernel	[7]
Uses Warp Shuffle for Commutator	Warp Shuffle	[7]
Compiles Cu Files Via	Build Rs	[7]
Has12 Kernels	12	[7]
Is Nvidia Gpu Framework	true	[8]
Is Live	True	[9]
Has Component	Warp	[10]
Being Figured Out	Cuda	[11]
Is Path to	push into the billions pts/s	[12]
Previous Benchmark Platform	null	[13]
Is Accelerator	true	[14]
Used for	Move Model and Data to Gpu	[27]
Type of	Gpu	[28]
Type	gpu-acceleration	[31]
Is Device for	Gpu	[32]
Monitored by	Profiler	[33]

Timeline axis is valid_time — when each source says the fact was true in the world, not when Dontopedia learned about it. Retracted rows are kept for provenance; coloured stripes indicate the context kind.

59 undated

—

preferredOverRocmblah/resources/part-13

ex:roc-m

—

avoidsRocmPainblah/resources/part-13

ex:roc-m

—

fasterThanblah/training-and-evals/part-31

ex:helios

—

servesAsBaselineblah/training-and-evals/part-31

ex:helios-benchmark

—

resumesFromblah/unturf/part-68

ex:checkpoint

—

nvidiaContextblah/watt-activation/part-104

ex:mps-vs-cuda

—

benefitsFromFftParallelismblah/watt-activation/part-100

ex:torch-cumsum-poor

—

referencesNvidiaComputePlatformblah/watt-activation/part-324

null

—

usesCudarcCrateblah/watt-activation/part-536

ex:cudarc-crate

—

superiorInGridSupportblah/watt-activation/part-536

ex:metal-kernel

—

hasNative2d3dGridSupportblah/watt-activation/part-536

ex:native-2d-3d-grid-support

—

hasSpecificWinsblah/watt-activation/part-536

ex:shared-memory-halo-loading

—

historicalAlternativeToMetalblah/watt-activation/part-536

ex:metal-gpu

—

comparedSuperiorToblah/watt-activation/part-536

ex:metal-kernel

—

usesWarpShuffleForCommutatorblah/watt-activation/part-536

ex:warp-shuffle

—

compilesCuFilesViablah/watt-activation/part-536

ex:build-rs

—

has12Kernelsblah/watt-activation/part-536

—

isNvidiaGpuFrameworkblah/watt-activation/part-535

true

—

isLiveblah/watt-activation/part-547

ex:true

—

hasComponentblah/watt-activation/part-586

ex:warp

—

beingFiguredOutblah/watt-activation/part-587

ex:cuda

—

isPathToblah/watt-activation/part-544

push into the billions pts/s

—

previousBenchmarkPlatformblah/watt-activation/part-475

null

—

isAcceleratorbeam/7086b533-5e24-4160-8df0-c927a68eff61

true

—

typeblah/random/33

ex:Technology

—

typeblah/resources/44

ex:SoftwarePlatform

—

labelblah/training-and-evals/32

CUDA

—

typeblah/training-and-evals/31

ex:SoftwarePlatform

—

labelblah/training-and-evals/31

CUDA

—

typeblah/unturf/68

ex:Technology

—

typeblah/vidya/9

ex:SoftwarePlatform

—

labelblah/vidya/9

CUDA

—

typeblah/watt-activation/266

ex:ComputePlatform

—

labelblah/watt-activation/266

cuda

—

labelblah/watt-activation/322

CUDA

—

typeblah/watt-activation/473

ex:Platform

—

typebeam/5a00c51f-dd1e-428b-b79b-370b9163f60f

ex:DeviceType

—

typebeam/c6ee25c2-5292-4256-95f3-8b4c1563623a

ex:GPUAccelerator

—

typebeam/e04766e0-b70f-4cd4-93df-3375bb36ef45

ex:Device

—

labelbeam/e04766e0-b70f-4cd4-93df-3375bb36ef45

CUDA

—

typebeam/095c6510-ee44-4498-9f43-8c628d14a869

ex:Function

—

labelbeam/095c6510-ee44-4498-9f43-8c628d14a869

cuda()

—

usedForbeam/095c6510-ee44-4498-9f43-8c628d14a869

ex:move-model-and-data-to-gpu

—

typeOfbeam/37089ae6-6ce4-42e5-87a2-1cfd71693a4d

ex:gpu

—

typebeam/37089ae6-6ce4-42e5-87a2-1cfd71693a4d

ex:GPUPlatform

—

labelbeam/37089ae6-6ce4-42e5-87a2-1cfd71693a4d

CUDA

—

typebeam/343cede3-dc11-4e37-89af-916034a8c42b

ex:GPU-Platform

—

typebeam/0a6354af-a6f7-4051-8cb3-e50345232784

ex:Method

—

labelbeam/0a6354af-a6f7-4051-8cb3-e50345232784

cuda()

—

typebeam/8b6abd69-54a1-41b8-bb85-d0b80bff1a3a

gpu-acceleration

—

hasFunctionbeam/af924c4f-8579-4b2a-85d1-c042076b09c7

ex:empty_cache

—

isDeviceForbeam/af924c4f-8579-4b2a-85d1-c042076b09c7

ex:GPU

—

typebeam/af924c4f-8579-4b2a-85d1-c042076b09c7

ex:AcceleratorDevice

—

monitored_bybeam/52c84698-6e15-4ede-b13e-73899fcfb7a4

ex:profiler

—

typebeam/80e4b051-0931-49af-8359-38149d7a6361

ex:GPUAcceleratedModule

—

labelbeam/80e4b051-0931-49af-8359-38149d7a6361

cuda

—

hasFunctionbeam/80e4b051-0931-49af-8359-38149d7a6361

ex:empty_cache

—

typebeam/4982f430-a6a9-4a69-bca4-91f76574ce61

ex:GPU-platform

—

typebeam/24776806-43b0-491e-806d-e4f4e8d75851

ex:GPUAccelerator

References (36)

36 references

[1]Part 132 facts
ctx:discord/blah/resources/part-13
[2]Part 312 facts
ctx:discord/blah/training-and-evals/part-31
[3]Part 681 fact
ctx:discord/blah/unturf/part-68
[4]Part 1041 fact
ctx:discord/blah/watt-activation/part-104
[5]Part 1001 fact
ctx:discord/blah/watt-activation/part-100
[6]Part 3241 fact
ctx:discord/blah/watt-activation/part-324
[7]Part 5369 facts
ctx:discord/blah/watt-activation/part-536
[8]Part 5351 fact
ctx:discord/blah/watt-activation/part-535
[9]Part 5471 fact
ctx:discord/blah/watt-activation/part-547
[10]Part 5861 fact
ctx:discord/blah/watt-activation/part-586
[11]Part 5871 fact
ctx:discord/blah/watt-activation/part-587
[12]Part 5441 fact
ctx:discord/blah/watt-activation/part-544
[13]Part 4751 fact
ctx:discord/blah/watt-activation/part-475
[14]7086b533 5e24 4160 8df0 C927a68eff611 fact
ctx:claims/beam/7086b533-5e24-4160-8df0-c927a68eff61
- full textbeam-chunk
  text/plain1 KBdoc:beam/7086b533-5e24-4160-8df0-c927a68eff61
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  # Load pre-trained model and tokenizer model_name = "bert-base-uncased" model = AutoModel.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Move the model to GPU if available device = torch.device("cuda" …
[15]331 fact
ctx:discord/blah/random/33
- full textrandom-33
  text/plain3 KBdoc:agent/random-33/e6bd1376-8597-472a-8c33-f3e7a058ef17
  Show excerpt
  [2026-03-02 19:20] xenonfun: generates vastly cleaner, didn't have to rerun anything, just a little slower than real-time, it uses MPS so helps some. (files: Screenshot_2026-03-02_at_2.06.10_PM.png, voice_clone_output_2.ogg) [2026-03-03 01…
[16]441 fact
ctx:discord/blah/resources/44
- full textresources-44
  text/plain3 KBdoc:agent/resources-44/573f3e0d-ea39-43c0-bb5e-0062a2db8d99
  Show excerpt
  [2026-03-02 21:56] uncloseai [bot]: Unknown tool **once**. Available: export-html [2026-03-02 21:56] uncloseai [bot]: Unknown tool **once**. Available: export-html, gather-screenshots, sweep-all-your-messages [2026-03-02 21:56] uncloseai [b…
[17]321 fact
ctx:discord/blah/training-and-evals/32
- full texttraining-and-evals-32
  text/plain2 KBdoc:agent/training-and-evals-32/ab593bf1-698c-4792-b6ac-21d15440d8eb
  Show excerpt
  [2026-02-28 15:19] ajaxdavis: (files: message.txt) [2026-02-28 23:17] foxhop.: https://microgpt.ai.unturf.com/v1/models [2026-02-28 23:18] foxhop.: (files: message.txt) [2026-02-28 23:19] foxhop.: look at dem JSON boyos & girlie [2026-02-…
[18]312 facts
ctx:discord/blah/training-and-evals/31
- full texttraining-and-evals-31
  text/plain3 KBdoc:agent/training-and-evals-31/74c05bc1-1ea4-4f79-968c-f5a35125b347
  Show excerpt
  [2026-02-27 01:43] ajaxdavis: still letiting my symbio run but also booting up another training run ``` Instance: alpha-cognitive Hardware: - GPU: NVIDIA L4 — 24GB VRAM, 121 FP16 TFLOPS - Machine type: g2-standard-4 — 4 vCPUs, 16GB…
[19]681 fact
ctx:discord/blah/unturf/68
- full textunturf-68
  text/plain3 KBdoc:agent/unturf-68/62404360-bbb9-4286-bf7b-a9e82fcc18a6
  Show excerpt
  [2026-03-12 16:35] xenonfun: I'm to codex for day or opencode go sub [2026-03-12 16:35] foxhop.: I have too much "speed of thought" in flux so I am just going to pay and wrap things up for the day [2026-03-12 16:35] foxhop.: however much th…
[20]92 facts
ctx:discord/blah/vidya/9
- full textvidya-9
  text/plain3 KBdoc:agent/vidya-9/b7b0c314-5b47-44f3-9679-7538a900a73d
  Show excerpt
  [2026-02-28 06:34] ajaxdavis: i don't know shit about any physical ones tbh ask lisa and richard [2026-02-28 06:35] ajaxdavis: lisa is using that 3060 [2026-02-28 06:37] rolandnsharp7643: I'm looking at buying this. not an NVIDIA card: http…
[21]2662 facts
ctx:discord/blah/watt-activation/266
- full textwatt-activation-266
  text/plain2 KBdoc:agent/watt-activation-266/0dd3318c-38a8-4ab0-8b7a-743748e72c54
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  [2026-03-13 07:20] xenonfun: • Ran it. Long-prompt test (context_patches=128000, prompt = 1,024,000 bytes, generated 64 patches, compiled cached decode): - prompt bytes: 1,024,000 - generated patches: 64 - total elapsed: 231.8s …
[22]3221 fact
ctx:discord/blah/watt-activation/322
- full textwatt-activation-322
  text/plain1 KBdoc:agent/watt-activation-322/761781b8-f4bc-47b4-88cf-16d056285449
  Show excerpt
  [2026-03-15 03:39] lisamegawatts: o your question about the telegrapher's equation — yes, that's exactly the right idea. The FFT convolution is our bottleneck. The telegrapher's equation gives us a closed-form wave propagation solution: ∂…
[23]4731 fact
ctx:discord/blah/watt-activation/473
- full textwatt-activation-473
  text/plain2 KBdoc:agent/watt-activation-473/bbee128e-eb0e-43a7-904e-88cd885d13dd
  Show excerpt
  [2026-03-21 19:47] xenonfun: ``` ⏺ Both done. Side-by-side comparison: ┌──────────┬─────────────┬────────────┐ │ │ Finite-diff │ Analytical │ ├──────────┼─────────────┼────────────┤ │ Best BPB │ 2.04 │ 2.19 │ …
[24]5a00c51f Dd1e 428b B79b 370b9163f60f1 fact
ctx:claims/beam/5a00c51f-dd1e-428b-b79b-370b9163f60f
[25]C6ee25c2 5292 4256 95f3 8b4c1563623a1 fact
ctx:claims/beam/c6ee25c2-5292-4256-95f3-8b4c1563623a
- full textbeam-chunk
  text/plain1 KBdoc:beam/c6ee25c2-5292-4256-95f3-8b4c1563623a
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  class ResizingModule(nn.Module): def __init__(self): super(ResizingModule, self).__init__() self.fc1 = nn.Linear(512, 128) self.fc2 = nn.Linear(128, 128) def forward(self, x): x = torch.relu(self.fc1…
[26]E04766e0 B70f 4cd4 93df 3375bb36ef452 facts
ctx:claims/beam/e04766e0-b70f-4cd4-93df-3375bb36ef45
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  text/plain1 KBdoc:beam/e04766e0-b70f-4cd4-93df-3375bb36ef45
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  results.extend(batch_results.cpu().numpy()) return results # Parallel processing def parallel_infer(texts, num_workers=4): with ThreadPoolExecutor(max_workers=num_workers) as executor: results = list(executor.map(in…
[27]095c6510 Ee44 4498 9f43 8c628d14a8693 facts
ctx:claims/beam/095c6510-ee44-4498-9f43-8c628d14a869
- full textbeam-chunk
  text/plain1 KBdoc:beam/095c6510-ee44-4498-9f43-8c628d14a869
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  - After each process completes its updates, synchronize the model and optimizer states. ### Key Points: - **Batch Size**: Adjust the batch size to balance between computational efficiency and memory usage. - **Number of Workers**: Adju…
[28]37089ae6 6ce4 42e5 87a2 1cfd71693a4d3 facts
ctx:claims/beam/37089ae6-6ce4-42e5-87a2-1cfd71693a4d
- full textbeam-chunk
  text/plain1 KBdoc:beam/37089ae6-6ce4-42e5-87a2-1cfd71693a4d
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  5. **Parallel Processing**: - Utilize multi-threading or multi-processing for data loading. Here's an optimized version of your code: ### Optimized Code ```python import torch import torch.nn as nn import torch.optim as optim from tor…
[29]343cede3 Dc11 4e37 89af 916034a8c42b1 fact
ctx:claims/beam/343cede3-dc11-4e37-89af-916034a8c42b
[30]0a6354af A6f7 4051 8cb3 E503452327842 facts
ctx:claims/beam/0a6354af-a6f7-4051-8cb3-e50345232784
[31]8b6abd69 54a1 41b8 Bb85 D0b80bff1a3a1 fact
ctx:claims/beam/8b6abd69-54a1-41b8-bb85-d0b80bff1a3a
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  loss = criterion(outputs, batch_targets) # Normalize the loss because it is accumulated loss = loss / accumulation_steps # Backward pass loss.backward() # Update wei…
[32]Af924c4f 8579 4b2a 85d1 C042076b09c73 facts
ctx:claims/beam/af924c4f-8579-4b2a-85d1-c042076b09c7
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  loss = loss / accumulation_steps # Backward pass scaler.scale(loss).backward() # Update weights if (i + 1) % accumulation_steps == 0: scaler.step(optimizer) …
[33]52c84698 6e15 4ede B13e 73899fcfb7a41 fact
ctx:claims/beam/52c84698-6e15-4ede-b13e-73899fcfb7a4
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  # Periodically empty the cache if (i + 1) % 100 == 0: torch.cuda.empty_cache() # Print profiling results print(prof.key_averages().table(sort_by="self_cuda_time_total")) ``` …
[34]80e4b051 0931 49af 8359 38149d7a63613 facts
ctx:claims/beam/80e4b051-0931-49af-8359-38149d7a6361
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  with profiler.profile(record_shapes=True, use_cuda=True) as prof: with profiler.record_function("model_training"): for i, (batch_inputs, batch_targets) in enumerate(dataloader): with autocast(): # Us…
[35]4982f430 A6a9 4a69 Bca4 91f76574ce611 fact
ctx:claims/beam/4982f430-a6a9-4a69-bca4-91f76574ce61
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  Here's how you can implement these optimizations: #### 1. Batch Processing Process multiple texts in a single batch to take advantage of parallel processing. #### 2. Model Quantization Use quantization to reduce the precision of the mod…
[36]24776806 43b0 491e 806d E4f4e8d758511 fact
ctx:claims/beam/24776806-43b0-491e-806d-e4f4e8d75851

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CUDA

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Inbound mentions (49)

isDeployedOnIs Deployed on(3)

fasterImplementationFaster Implementation(2)

hasModuleHas Module(2)

referencesHardwareReferences Hardware(2)

abstractsAbstracts(1)

aimsToBeatAims to Beat(1)

beingFiguredOutBeing Figured Out(1)

canBeCan Be(1)

checksAvailabilityChecks Availability(1)

comparedToCompared to(1)

comparesCompares(1)

comparesToCompares to(1)

compatibleWithoutChangesCompatible Without Changes(1)

competesWithCompetes With(1)

configuredForConfigured for(1)

contrastsWithContrasts With(1)

didntWantToRunDidnt Want to Run(1)

enabledByEnabled by(1)

enablesDispatchForEnables Dispatch for(1)

hasLoserHas Loser(1)

hasPoorGPUParallelismHas Poor Gpu Parallelism(1)

hasSupportForHas Support for(1)

implicatesBetterPerformanceImplicates Better Performance(1)

involvesFrameworkInvolves Framework(1)

isMovedToIs Moved to(1)

isMovedToDeviceIs Moved to Device(1)

isOptimizedForIs Optimized for(1)

isReadyForIs Ready for(1)

isSpecificToIs Specific to(1)

needsDecouplingForCudaNeeds Decoupling for Cuda(1)

originallyTargetsOriginally Targets(1)

parallelismWinsOnParallelism Wins on(1)

plansToCompareBenchmarkPlans to Compare Benchmark(1)

playsWithPlays With(1)

preferredDevicePreferred Device(1)

presupposesExistenceOfPresupposes Existence of(1)

requiresDecouplingRequires Decoupling(1)

shouldBeUsedFromStartShould Be Used From Start(1)

suggestsAvoidingSuggests Avoiding(1)

targetsTargets(1)

targetsDeviceTargets Device(1)

usesCInsteadOfUses C Instead of(1)

usesCudaUses Cuda(1)

warnsAboutTweakWarns About Tweak(1)

Other facts (31)

Timeline

References (36)

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Rdf:typein dispute`rdf:type`

`isDeployedOn`Is Deployed on(3)

`fasterImplementation`Faster Implementation(2)

`hasModule`Has Module(2)

`referencesHardware`References Hardware(2)

`abstracts`Abstracts(1)

`aimsToBeat`Aims to Beat(1)

`beingFiguredOut`Being Figured Out(1)

`canBe`Can Be(1)

`checksAvailability`Checks Availability(1)

`comparedTo`Compared to(1)

`compares`Compares(1)

`comparesTo`Compares to(1)

`compatibleWithoutChanges`Compatible Without Changes(1)

`competesWith`Competes With(1)

`configuredFor`Configured for(1)

`contrastsWith`Contrasts With(1)

`didntWantToRun`Didnt Want to Run(1)

`enabledBy`Enabled by(1)

`enablesDispatchFor`Enables Dispatch for(1)

`hasLoser`Has Loser(1)

`hasPoorGPUParallelism`Has Poor Gpu Parallelism(1)

`hasSupportFor`Has Support for(1)

`implicatesBetterPerformance`Implicates Better Performance(1)

`involvesFramework`Involves Framework(1)

`isMovedTo`Is Moved to(1)

`isMovedToDevice`Is Moved to Device(1)

`isOptimizedFor`Is Optimized for(1)

`isReadyFor`Is Ready for(1)

`isSpecificTo`Is Specific to(1)

`needsDecouplingForCuda`Needs Decoupling for Cuda(1)

`originallyTargets`Originally Targets(1)

`parallelismWinsOn`Parallelism Wins on(1)

`plansToCompareBenchmark`Plans to Compare Benchmark(1)

`playsWith`Plays With(1)

`preferredDevice`Preferred Device(1)

`presupposesExistenceOf`Presupposes Existence of(1)

`requiresDecoupling`Requires Decoupling(1)

`shouldBeUsedFromStart`Should Be Used From Start(1)

`suggestsAvoiding`Suggests Avoiding(1)

`targets`Targets(1)

`targetsDevice`Targets Device(1)

`usesCInsteadOf`Uses C Instead of(1)

`usesCuda`Uses Cuda(1)

`warnsAboutTweak`Warns About Tweak(1)