Programming Concurrency on GPUs with Rust : Examine, Run, and Measure GPU Workloads Using CUDA, Streams, and Async Pipelines
Language: English
Published by Gitforgits, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 72.57
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - The bottleneck is not your kernel. In fact, it almost never is.After spending enough time in Nsight, the way you read a timeline becomes as intuitive as a mechanic listening to an engine. The same faults keep cropping up, such as a GPU with thousands of cores sitting idle behind a blocking copy, a single default stream or a host thread frozen at a synchronisation point that was never necessary. This book is about identifying that fault early and eliminating it.Designed for programming professionals, this book is written for GPU developers, Rust engineers and AI practitioners who want their devices to be busy, not just correct. Our approach is to stay in pure Rust throughout, using cuda-oxide for SIMT kernels and cutile-rs for the tile model. We also rely heavily on the type system to ensure that data races fail at compile time rather than at 3 AM during production. We will overlap transfers with compute using streams and pinned memory, build a staging ring that pipelines every stage and feed the GPU through Tokio producers with bounded-channel backpressure. Each technique is profiled, verified against a CPU reference and measured.Key LearningsProfile before you optimise, and let Nsight Systems show you who is actually waiting.Write SIMT kernels in pure Rust where data races fail at compile time.Treat default stream as the enemy of overlap, and fork your own.Overlap copies with compute using pinned memory and a staging ring.Reach for reduction ladder instead of atomics when summing on the device.Express element-wise work as tile kernels, and let compiler map the hardware.Keep the GPU fed with Tokio producers and bounded-channel backpressure.Verify every kernel against CPU reference before you trust its timing.Weigh throughput against latency, since tuning for one quietly taxes the other.Know when to stop, because past a point concurrency only adds bookkeeping.Table of ContentRust's Threads, Timing and OwnershipCPU versus GPU ExecutionSIMT Kernels, Blocks and GridsRace-Free Parallel KernelsAsynchronous GPU ExecutionOverlapping Transfers with StreamsTile Programming with cutile-rsAsync GPU Pipelines with TokioThroughput, Latency and BottlenecksTarget AudienceIf you're a programmer looking for a hands-on guide to GPU programming, Rust techniques and AI principles, this book is for you. It's designed to help you make the most of your device's processing power.…
Seller Inventory # 9788117136058
- Title
- Programming Concurrency on GPUs with Rust : Examine, Run, and Measure GPU Workloads Using CUDA, Streams, and Async Pipelines
- Author
- Zyle Kot
- Publisher
- Gitforgits
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 8117136058
- ISBN 13
- 9788117136058
- Item weight
- 316 grams
- Dimensions
- 235x191x9 mm
The bottleneck is not your kernel. In fact, it almost never is.
After spending enough time in Nsight, the way you read a timeline becomes as intuitive as a mechanic listening to an engine. The same faults keep cropping up, such as a GPU with thousands of cores sitting idle behind a blocking copy, a single default stream or a host thread frozen at a synchronisation point that was never necessary. This book is about identifying that fault early and eliminating it.
Designed for programming professionals, this book is written for GPU developers, Rust engineers and AI practitioners who want their devices to be busy, not just correct. Our approach is to stay in pure Rust throughout, using cuda-oxide for SIMT kernels and cutile-rs for the tile model. We also rely heavily on the type system to ensure that data races fail at compile time rather than at 3 AM during production. We will overlap transfers with compute using streams and pinned memory, build a staging ring that pipelines every stage and feed the GPU through Tokio producers with bounded-channel backpressure. Each technique is profiled, verified against a CPU reference and measured.
Key Learnings
- Profile before you optimise, and let Nsight Systems show you who is actually waiting.
- Write SIMT kernels in pure Rust where data races fail at compile time.
- Treat default stream as the enemy of overlap, and fork your own.
- Overlap copies with compute using pinned memory and a staging ring.
- Reach for reduction ladder instead of atomics when summing on the device.
- Express element-wise work as tile kernels, and let compiler map the hardware.
- Keep the GPU fed with Tokio producers and bounded-channel backpressure.
- Verify every kernel against CPU reference before you trust its timing.
- Weigh throughput against latency, since tuning for one quietly taxes the other.
- Know when to stop, because past a point concurrency only adds bookkeeping.
Table of Content
- Rust’s Threads, Timing and Ownership
- CPU versus GPU Execution
- SIMT Kernels, Blocks and Grids
- Race-Free Parallel Kernels
- Asynchronous GPU Execution
- Overlapping Transfers with Streams
- Tile Programming with cutile-rs
- Async GPU Pipelines with Tokio
- Throughput, Latency and Bottlenecks
Target Audience
If you're a programmer looking for a hands-on guide to GPU programming, Rust techniques and AI principles, this book is for you. It's designed to help you make the most of your device's processing power."Synopsis" may belong to another edition of this title.
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