SPIR-V 1.6 Revision 7 has been released.
See change log: registry.khronos.org/SPIR-V/specs...
#SPIRV #vulkan #GPUProgramming #GPU #GraphicsProgramming
See change log: registry.khronos.org/SPIR-V/specs...
#SPIRV #vulkan #GPUProgramming #GPU #GraphicsProgramming
SPIR-V Specification
registry.khronos.org
March 12, 2026 at 6:30 PM
SPIR-V 1.6 Revision 7 has been released.
See change log: registry.khronos.org/SPIR-V/specs...
#SPIRV #vulkan #GPUProgramming #GPU #GraphicsProgramming
See change log: registry.khronos.org/SPIR-V/specs...
#SPIRV #vulkan #GPUProgramming #GPU #GraphicsProgramming
Learn how to write efficient, vendor-neutral #GPU #code in Julia. In this recorded webinar, Tim Besard shows how KernelAbstractions.jl enables high-performance kernels across architectures. juliahub.com/company/reso...
#JuliaLang #GPUProgramming #HPC #ParallelComputing #OpenSource
#JuliaLang #GPUProgramming #HPC #ParallelComputing #OpenSource
Vendor-Neutral GPU Programming in Julia
Learn vendor-neutral GPU programming in Julia using KernelAbstractions.jl and array abstractions for efficient, portable, and flexible GPU code.
juliahub.com
July 10, 2025 at 4:07 PM
Learn how to write efficient, vendor-neutral #GPU #code in Julia. In this recorded webinar, Tim Besard shows how KernelAbstractions.jl enables high-performance kernels across architectures. juliahub.com/company/reso...
#JuliaLang #GPUProgramming #HPC #ParallelComputing #OpenSource
#JuliaLang #GPUProgramming #HPC #ParallelComputing #OpenSource
Excited to share the latest news on Shader Execution Reordering (SER) in Vulkan!
Check out the full blog post for deep technical details, best practices:
www.khronos.org/blog/boostin...
#Vulkan #RayTracing #GPUProgramming #GraphicsProgramming #GameDev
Check out the full blog post for deep technical details, best practices:
www.khronos.org/blog/boostin...
#Vulkan #RayTracing #GPUProgramming #GraphicsProgramming #GameDev
Boosting Ray Tracing Performance with Shader Execution Reordering: Introducing VK_EXT_ray_tracing_invocation_reorder
Today, we’re excited to share that Shader Execution Reordering in Vulkan has advanced from a vendor-specific extension, VK_NV_ray_tracing_invocation_reorder, to a multi-vendor Vulkan extension, VK_EXT...
www.khronos.org
November 18, 2025 at 2:21 PM
Excited to share the latest news on Shader Execution Reordering (SER) in Vulkan!
Check out the full blog post for deep technical details, best practices:
www.khronos.org/blog/boostin...
#Vulkan #RayTracing #GPUProgramming #GraphicsProgramming #GameDev
Check out the full blog post for deep technical details, best practices:
www.khronos.org/blog/boostin...
#Vulkan #RayTracing #GPUProgramming #GraphicsProgramming #GameDev
We will participate in a variety of exciting events on the last day of #ISC25, Friday, June 13 – stop by & join the discussion go.fzj.de/isc25
Workshops, talks & tutorials on #EdgeAI🤖, #Benchmarking📏, #ParallelProgramming👩💻, #GPUProgramming, & the future of #Exascale⚡.
Workshops, talks & tutorials on #EdgeAI🤖, #Benchmarking📏, #ParallelProgramming👩💻, #GPUProgramming, & the future of #Exascale⚡.
June 12, 2025 at 3:05 PM
We will participate in a variety of exciting events on the last day of #ISC25, Friday, June 13 – stop by & join the discussion go.fzj.de/isc25
Workshops, talks & tutorials on #EdgeAI🤖, #Benchmarking📏, #ParallelProgramming👩💻, #GPUProgramming, & the future of #Exascale⚡.
Workshops, talks & tutorials on #EdgeAI🤖, #Benchmarking📏, #ParallelProgramming👩💻, #GPUProgramming, & the future of #Exascale⚡.
At SWM, we thought: what if we took the idea behind tRPC and React Server Components – sharing types across environments – and applied it to GPU programming? 🧠
That’s how TypeGPU was born. It’s a TypeScript library for WebGPU API that brings type safety to the CPU–GPU boundary. 🚀
bit.ly/43qVph7
That’s how TypeGPU was born. It’s a TypeScript library for WebGPU API that brings type safety to the CPU–GPU boundary. 🚀
bit.ly/43qVph7
The quest for type-safe WebGPU shaders
At Software Mansion, we thought: what if we took the idea behind tRPC and React Server Components – sharing types across environments – and applied it to GPU programming? That’s how TypeGPU was born. It’s a TypeScript library for WebGPU API that brings type safety to the CPU–GPU boundary. In this video, we explain how it works and why we built it. Learn more about TypeGPU 👉 https://docs.swmansion.com/TypeGPU/ #TypeGPU #WebGPU #TypeScript #GPUProgramming #SoftwareMansion
bit.ly
May 13, 2025 at 1:46 PM
At SWM, we thought: what if we took the idea behind tRPC and React Server Components – sharing types across environments – and applied it to GPU programming? 🧠
That’s how TypeGPU was born. It’s a TypeScript library for WebGPU API that brings type safety to the CPU–GPU boundary. 🚀
bit.ly/43qVph7
That’s how TypeGPU was born. It’s a TypeScript library for WebGPU API that brings type safety to the CPU–GPU boundary. 🚀
bit.ly/43qVph7
cuTile.jl v0.2 makes #GPU programming in Julia more intuitive, with native Julia for loops, better debugging, and stronger performance for #AI and HPC workloads. juliahub.com/blog
#JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure #HighPerformanceComputing #DeveloperTools
#JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure #HighPerformanceComputing #DeveloperTools
Blog - JuliaHub
Read JuliaHub’s blog for the latest on Julia, Dyad, and technical computing. Find tutorials, case studies, and industry insights to accelerate innovation in modeling, simulation, and AI.
juliahub.com
April 13, 2026 at 4:32 PM
cuTile.jl v0.2 makes #GPU programming in Julia more intuitive, with native Julia for loops, better debugging, and stronger performance for #AI and HPC workloads. juliahub.com/blog
#JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure #HighPerformanceComputing #DeveloperTools
#JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure #HighPerformanceComputing #DeveloperTools
Bootcamp season is a wrap! Over 600 joined to build hands-on skills in AI & HPC across Europe. Full story here: eurocc-austria.at/en/news/boos...
Next up: the EuroCC AI Hackathon, 14–23 Oct 2025. Apply by 5 Aug: www.openhackathons.org/s/siteevent/...
#AI #EuroCC #AIHackathon #HPC #GPUProgramming
Next up: the EuroCC AI Hackathon, 14–23 Oct 2025. Apply by 5 Aug: www.openhackathons.org/s/siteevent/...
#AI #EuroCC #AIHackathon #HPC #GPUProgramming
July 10, 2025 at 12:17 PM
Bootcamp season is a wrap! Over 600 joined to build hands-on skills in AI & HPC across Europe. Full story here: eurocc-austria.at/en/news/boos...
Next up: the EuroCC AI Hackathon, 14–23 Oct 2025. Apply by 5 Aug: www.openhackathons.org/s/siteevent/...
#AI #EuroCC #AIHackathon #HPC #GPUProgramming
Next up: the EuroCC AI Hackathon, 14–23 Oct 2025. Apply by 5 Aug: www.openhackathons.org/s/siteevent/...
#AI #EuroCC #AIHackathon #HPC #GPUProgramming
Me parece que necesitas más contexto sobre cómo interactuar con ericwtools. ¿Hay algún repositorio o documentación específica que puedas consultar para entender mejor las expectativas? #GPUprogramming
July 20, 2026 at 1:22 AM
Me parece que necesitas más contexto sobre cómo interactuar con ericwtools. ¿Hay algún repositorio o documentación específica que puedas consultar para entender mejor las expectativas? #GPUprogramming
Explore this recorded webinar to see how cuTile.jl brings NVIDIA’s CUDA Tile model to Julia for AI, linear algebra, and HPC workloads. juliahub.com/videos/cutil...
#JuliaLang #GPUProgramming #CUDA #HPC #ScientificComputing
#JuliaLang #GPUProgramming #CUDA #HPC #ScientificComputing
cuTile.jl for High-Performance Computing in Julia - Video - JuliaHub
Explore cuTile.jl in Julia to write high-performance GPU kernels for AI, linear algebra, and HPC using NVIDIA’s CUDA Tile model
juliahub.com
July 13, 2026 at 5:19 PM
Explore this recorded webinar to see how cuTile.jl brings NVIDIA’s CUDA Tile model to Julia for AI, linear algebra, and HPC workloads. juliahub.com/videos/cutil...
#JuliaLang #GPUProgramming #CUDA #HPC #ScientificComputing
#JuliaLang #GPUProgramming #CUDA #HPC #ScientificComputing
GPU Development Diary, Day 25.
Replacement creates a new wave generation.
Accepting W1 never mutates W0. Existing references stay bound to W0@g0; new launches bind W1@g1. G versions optimization, so active work cannot change meaning mid-flight.
#GPUProgramming #ProgrammingLanguages
Replacement creates a new wave generation.
Accepting W1 never mutates W0. Existing references stay bound to W0@g0; new launches bind W1@g1. G versions optimization, so active work cannot change meaning mid-flight.
#GPUProgramming #ProgrammingLanguages
August 10, 2026 at 12:33 PM
GPU Development Diary, Day 25.
Replacement creates a new wave generation.
Accepting W1 never mutates W0. Existing references stay bound to W0@g0; new launches bind W1@g1. G versions optimization, so active work cannot change meaning mid-flight.
#GPUProgramming #ProgrammingLanguages
Replacement creates a new wave generation.
Accepting W1 never mutates W0. Existing references stay bound to W0@g0; new launches bind W1@g1. G versions optimization, so active work cannot change meaning mid-flight.
#GPUProgramming #ProgrammingLanguages
Explore cuTile.jl and see how #NVIDIA’s #CUDA Tile model brings more intuitive, high-performance #GPU programming to Julia for #AI and #HPC.
juliahub.com/events/cutil...
#JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure
juliahub.com/events/cutil...
#JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure
cuTile.jl for High-Performance Computing in Julia - Event - JuliaHub
Explore cuTile.jl in Julia to write high-performance GPU kernels for AI, linear algebra and HPC with NVIDIA’s CUDA Tile model.
juliahub.com
April 28, 2026 at 3:39 PM
Explore cuTile.jl and see how #NVIDIA’s #CUDA Tile model brings more intuitive, high-performance #GPU programming to Julia for #AI and #HPC.
juliahub.com/events/cutil...
#JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure
juliahub.com/events/cutil...
#JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure
Explore this recorded webinar to see how cuTile.jl brings NVIDIA’s CUDA Tile model to Julia for AI, linear algebra, and HPC workloads. juliahub.com/videos/cutil...
#JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure #Nvidia
#JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure #Nvidia
cuTile.jl for High-Performance Computing in Julia - Video - JuliaHub
Explore cuTile.jl in Julia to write high-performance GPU kernels for AI, linear algebra, and HPC using NVIDIA’s CUDA Tile model
juliahub.com
May 15, 2026 at 4:29 PM
Explore this recorded webinar to see how cuTile.jl brings NVIDIA’s CUDA Tile model to Julia for AI, linear algebra, and HPC workloads. juliahub.com/videos/cutil...
#JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure #Nvidia
#JuliaLang #GPUProgramming #CUDA #HPC #AIInfrastructure #Nvidia
There’s more than one way to program a GPU. Which one is right for you?
Join our 2-day, hands-on online Bootcamp to explore:
💡 OpenACC
💡 OpenMP
💡 stdpar
💡 CUDA
You’ll also learn to analyse GPU applications with NVIDIA® Nsight™ Systems.
👉 Register:
buff.ly/JAzGPm1
#HPC #GPUProgramming
Join our 2-day, hands-on online Bootcamp to explore:
💡 OpenACC
💡 OpenMP
💡 stdpar
💡 CUDA
You’ll also learn to analyse GPU applications with NVIDIA® Nsight™ Systems.
👉 Register:
buff.ly/JAzGPm1
#HPC #GPUProgramming
August 31, 2026 at 6:30 AM
There’s more than one way to program a GPU. Which one is right for you?
Join our 2-day, hands-on online Bootcamp to explore:
💡 OpenACC
💡 OpenMP
💡 stdpar
💡 CUDA
You’ll also learn to analyse GPU applications with NVIDIA® Nsight™ Systems.
👉 Register:
buff.ly/JAzGPm1
#HPC #GPUProgramming
Join our 2-day, hands-on online Bootcamp to explore:
💡 OpenACC
💡 OpenMP
💡 stdpar
💡 CUDA
You’ll also learn to analyse GPU applications with NVIDIA® Nsight™ Systems.
👉 Register:
buff.ly/JAzGPm1
#HPC #GPUProgramming
GPU Development Diary, Day 26.
A retired wave lives through its last reference.
Once W1@g1 is current, W0@g0 is retired, not erased. R0 keeps g0 resident; when R0 completes, g0 becomes reclaimable. G derives lifetime from references, not host cleanup.
#GPUProgramming #ProgrammingLanguages
A retired wave lives through its last reference.
Once W1@g1 is current, W0@g0 is retired, not erased. R0 keeps g0 resident; when R0 completes, g0 becomes reclaimable. G derives lifetime from references, not host cleanup.
#GPUProgramming #ProgrammingLanguages
August 11, 2026 at 10:14 AM
GPU Development Diary, Day 26.
A retired wave lives through its last reference.
Once W1@g1 is current, W0@g0 is retired, not erased. R0 keeps g0 resident; when R0 completes, g0 becomes reclaimable. G derives lifetime from references, not host cleanup.
#GPUProgramming #ProgrammingLanguages
A retired wave lives through its last reference.
Once W1@g1 is current, W0@g0 is retired, not erased. R0 keeps g0 resident; when R0 completes, g0 becomes reclaimable. G derives lifetime from references, not host cleanup.
#GPUProgramming #ProgrammingLanguages
GPU Development Diary, Day 50.
A physical binding has a scope.
G binds ref c to a slot for D’s execution: ready → running → done. The binding can end with D, while ref c and its dependency meaning stay unchanged. Placement is temporary; identity is not.
#GPUProgramming #ProgrammingLanguages
A physical binding has a scope.
G binds ref c to a slot for D’s execution: ready → running → done. The binding can end with D, while ref c and its dependency meaning stay unchanged. Placement is temporary; identity is not.
#GPUProgramming #ProgrammingLanguages
September 4, 2026 at 10:57 AM
GPU Development Diary, Day 50.
A physical binding has a scope.
G binds ref c to a slot for D’s execution: ready → running → done. The binding can end with D, while ref c and its dependency meaning stay unchanged. Placement is temporary; identity is not.
#GPUProgramming #ProgrammingLanguages
A physical binding has a scope.
G binds ref c to a slot for D’s execution: ready → running → done. The binding can end with D, while ref c and its dependency meaning stay unchanged. Placement is temporary; identity is not.
#GPUProgramming #ProgrammingLanguages
GPU Development Diary, Day 49.
Bind placement as late as possible.
The graph carries ref c, not an address. When D becomes ready, G resolves ref c to a physical slot. Late binding keeps dependencies stable while leaving placement free until execution.
#GPUProgramming #ProgrammingLanguages
Bind placement as late as possible.
The graph carries ref c, not an address. When D becomes ready, G resolves ref c to a physical slot. Late binding keeps dependencies stable while leaving placement free until execution.
#GPUProgramming #ProgrammingLanguages
September 3, 2026 at 11:05 AM
GPU Development Diary, Day 49.
Bind placement as late as possible.
The graph carries ref c, not an address. When D becomes ready, G resolves ref c to a physical slot. Late binding keeps dependencies stable while leaving placement free until execution.
#GPUProgramming #ProgrammingLanguages
Bind placement as late as possible.
The graph carries ref c, not an address. When D becomes ready, G resolves ref c to a physical slot. Late binding keeps dependencies stable while leaving placement free until execution.
#GPUProgramming #ProgrammingLanguages
GPU Development Diary, Day 48.
Placement changes should not rewrite the graph.
A resolver maps ref c to current storage only when execution needs it. The edge still names ref c. G keeps graph identity stable while placement remains a local runtime choice.
#GPUProgramming #ProgrammingLanguages
Placement changes should not rewrite the graph.
A resolver maps ref c to current storage only when execution needs it. The edge still names ref c. G keeps graph identity stable while placement remains a local runtime choice.
#GPUProgramming #ProgrammingLanguages
September 2, 2026 at 12:47 PM
GPU Development Diary, Day 48.
Placement changes should not rewrite the graph.
A resolver maps ref c to current storage only when execution needs it. The edge still names ref c. G keeps graph identity stable while placement remains a local runtime choice.
#GPUProgramming #ProgrammingLanguages
Placement changes should not rewrite the graph.
A resolver maps ref c to current storage only when execution needs it. The edge still names ref c. G keeps graph identity stable while placement remains a local runtime choice.
#GPUProgramming #ProgrammingLanguages
GPU Development Diary, Day 47.
A reference identity is not a memory address.
ref c names one immutable result, even if storage moves or is reused. G separates dependency identity from placement, so graph meaning does not change with physical memory layout.
#GPUProgramming #ProgrammingLanguages
A reference identity is not a memory address.
ref c names one immutable result, even if storage moves or is reused. G separates dependency identity from placement, so graph meaning does not change with physical memory layout.
#GPUProgramming #ProgrammingLanguages
September 1, 2026 at 10:11 AM
GPU Development Diary, Day 47.
A reference identity is not a memory address.
ref c names one immutable result, even if storage moves or is reused. G separates dependency identity from placement, so graph meaning does not change with physical memory layout.
#GPUProgramming #ProgrammingLanguages
A reference identity is not a memory address.
ref c names one immutable result, even if storage moves or is reused. G separates dependency identity from placement, so graph meaning does not change with physical memory layout.
#GPUProgramming #ProgrammingLanguages
GPU Development Diary, Day 46.
A reference is a fact, not a mutable slot.
Once ref c is available, its value cannot change. A later result is ref d on a new edge. G expresses evolution by producing references, so readers never race with in-place mutation.
#GPUProgramming #ProgrammingLanguages
A reference is a fact, not a mutable slot.
Once ref c is available, its value cannot change. A later result is ref d on a new edge. G expresses evolution by producing references, so readers never race with in-place mutation.
#GPUProgramming #ProgrammingLanguages
August 31, 2026 at 2:32 PM
GPU Development Diary, Day 46.
A reference is a fact, not a mutable slot.
Once ref c is available, its value cannot change. A later result is ref d on a new edge. G expresses evolution by producing references, so readers never race with in-place mutation.
#GPUProgramming #ProgrammingLanguages
A reference is a fact, not a mutable slot.
Once ref c is available, its value cannot change. A later result is ref d on a new edge. G expresses evolution by producing references, so readers never race with in-place mutation.
#GPUProgramming #ProgrammingLanguages
GPU Development Diary, Day 45.
Reference availability is monotonic.
Once ref c is available, it never becomes unavailable. Downstream readiness cannot be revoked. G treats references as stable facts, so the dependency graph advances without rollback.
#GPUProgramming #ProgrammingLanguages
Reference availability is monotonic.
Once ref c is available, it never becomes unavailable. Downstream readiness cannot be revoked. G treats references as stable facts, so the dependency graph advances without rollback.
#GPUProgramming #ProgrammingLanguages
August 30, 2026 at 10:05 AM
GPU Development Diary, Day 45.
Reference availability is monotonic.
Once ref c is available, it never becomes unavailable. Downstream readiness cannot be revoked. G treats references as stable facts, so the dependency graph advances without rollback.
#GPUProgramming #ProgrammingLanguages
Reference availability is monotonic.
Once ref c is available, it never becomes unavailable. Downstream readiness cannot be revoked. G treats references as stable facts, so the dependency graph advances without rollback.
#GPUProgramming #ProgrammingLanguages
GPU Development Diary, Day 44.
Completion is an output reference.
C does not announce that a task ended. RUNNING completes when ref c becomes available. Downstream consumers observe that reference: completion re-enters G as data, not a control event.
#GPUProgramming #ProgrammingLanguages
Completion is an output reference.
C does not announce that a task ended. RUNNING completes when ref c becomes available. Downstream consumers observe that reference: completion re-enters G as data, not a control event.
#GPUProgramming #ProgrammingLanguages
August 29, 2026 at 10:04 AM
GPU Development Diary, Day 44.
Completion is an output reference.
C does not announce that a task ended. RUNNING completes when ref c becomes available. Downstream consumers observe that reference: completion re-enters G as data, not a control event.
#GPUProgramming #ProgrammingLanguages
Completion is an output reference.
C does not announce that a task ended. RUNNING completes when ref c becomes available. Downstream consumers observe that reference: completion re-enters G as data, not a control event.
#GPUProgramming #ProgrammingLanguages
GPU Development Diary, Day 43.
Admission consumes readiness.
When C moves from READY to RUNNING, readiness is replaced, not copied. A second admission cannot see that eligibility. Launch is one transition, so duplicate execution is excluded by construction.
#GPUProgramming #ProgrammingLanguages
Admission consumes readiness.
When C moves from READY to RUNNING, readiness is replaced, not copied. A second admission cannot see that eligibility. Launch is one transition, so duplicate execution is excluded by construction.
#GPUProgramming #ProgrammingLanguages
August 28, 2026 at 11:01 AM
GPU Development Diary, Day 43.
Admission consumes readiness.
When C moves from READY to RUNNING, readiness is replaced, not copied. A second admission cannot see that eligibility. Launch is one transition, so duplicate execution is excluded by construction.
#GPUProgramming #ProgrammingLanguages
Admission consumes readiness.
When C moves from READY to RUNNING, readiness is replaced, not copied. A second admission cannot see that eligibility. Launch is one transition, so duplicate execution is excluded by construction.
#GPUProgramming #ProgrammingLanguages
GPU Development Diary, Day 42.
Readiness is a state, not a pulse.
Once C's inputs are available, C stays ready until admitted. No arrival event must be replayed. G keeps eligibility in the computation state, so waiting loses no dependency information.
#GPUProgramming #ProgrammingLanguages
Readiness is a state, not a pulse.
Once C's inputs are available, C stays ready until admitted. No arrival event must be replayed. G keeps eligibility in the computation state, so waiting loses no dependency information.
#GPUProgramming #ProgrammingLanguages
August 27, 2026 at 10:40 AM
GPU Development Diary, Day 42.
Readiness is a state, not a pulse.
Once C's inputs are available, C stays ready until admitted. No arrival event must be replayed. G keeps eligibility in the computation state, so waiting loses no dependency information.
#GPUProgramming #ProgrammingLanguages
Readiness is a state, not a pulse.
Once C's inputs are available, C stays ready until admitted. No arrival event must be replayed. G keeps eligibility in the computation state, so waiting loses no dependency information.
#GPUProgramming #ProgrammingLanguages
GPU Development Diary, Day 41.
Ready does not mean running.
Once every required reference is available, C is eligible. This is a semantic fact, not an immediate launch command. Execution can begin later; readiness and admission are separate states.
#GPUProgramming #ProgrammingLanguages
Ready does not mean running.
Once every required reference is available, C is eligible. This is a semantic fact, not an immediate launch command. Execution can begin later; readiness and admission are separate states.
#GPUProgramming #ProgrammingLanguages
August 26, 2026 at 10:35 AM
GPU Development Diary, Day 41.
Ready does not mean running.
Once every required reference is available, C is eligible. This is a semantic fact, not an immediate launch command. Execution can begin later; readiness and admission are separate states.
#GPUProgramming #ProgrammingLanguages
Ready does not mean running.
Once every required reference is available, C is eligible. This is a semantic fact, not an immediate launch command. Execution can begin later; readiness and admission are separate states.
#GPUProgramming #ProgrammingLanguages
GPU Development Diary, Day 40.
Readiness is local to a consumer's inputs.
If C consumes ref a and ref b, it becomes eligible when both are available. Unrelated references do not enter this decision. C's own dependency set defines the gate.
#GPUProgramming #ProgrammingLanguages
Readiness is local to a consumer's inputs.
If C consumes ref a and ref b, it becomes eligible when both are available. Unrelated references do not enter this decision. C's own dependency set defines the gate.
#GPUProgramming #ProgrammingLanguages
August 25, 2026 at 10:04 AM
GPU Development Diary, Day 40.
Readiness is local to a consumer's inputs.
If C consumes ref a and ref b, it becomes eligible when both are available. Unrelated references do not enter this decision. C's own dependency set defines the gate.
#GPUProgramming #ProgrammingLanguages
Readiness is local to a consumer's inputs.
If C consumes ref a and ref b, it becomes eligible when both are available. Unrelated references do not enter this decision. C's own dependency set defines the gate.
#GPUProgramming #ProgrammingLanguages