modern CPU instruction cache. As a result, the hardware spends a considerable amount of processing time — nearly 30 percent, in many cases — getting an instruction stream from memory to the CPU.” Accelerate refactoring. 33Quick benchmark https://quick-bench.com/q/mOAHh7zZeagJlCJ63GtWVMPngsw 34 25% overhead {deferred_construction output; read_strings(in, out(output)); https://www.foonathan QWORD PTR [rdi]Quick benchmark https://quick-bench.com/q/pJ3z9L_Q1M16qob8-sg8lM3-T60 44 200% overhead {C++ Core Guidelines F.26: Use a unique_ptr to transfer ownership where a pointer is needed

1/66Bridging the Gap: Writing Portable Programs for CPU and GPU using CUDA Thomas Mejstrik Sebastian Woblistin 2/66Content 1 Motivation Audience etc.. Cuda crash course Quiz time 2 Patterns Oldschool afterwards7/66 Motivation Patterns The dark path Cuda proposal Thank you Why write programs for CPU and GPU Difference CPU/GPU Algorithms are designed differently Latency/Throughput Memory bandwidth Number of talk7/66 Motivation Patterns The dark path Cuda proposal Thank you Why write programs for CPU and GPU Difference CPU/GPU Why it makes sense? Library/Framework developers Embarrassingly parallel algorithms

Is std::mdspan a Zero-overhead Abstraction? Oleksandr Bacherikov Snap IncWhat is std::mdspan? It’s a view over a multi-dimensional array. It’s designed primarily to be used as a function parameter 24 Wrong! std::layout_stride supports only all strides specified at runtime. If we target zero overhead, we have to specify one of the strides as 1 at compile time.What does the Standard offer us instead

interpreter a JiT, or AoT? What if it modifies its bytecode? A CPU executes machine code… an interpreter executes bytecode… That’s the same thing, a CPU is an interpreter for machine code. A compiler can perform factor of 7 to 20. How do you develop hardware that doesn’t exist yet? When I started working on a CPU, it wasn’t obvious to me that you do so with simulators—multiple simulators—which simulate what the to the performance level of -O4.” “ Rosenblum et al. 1995 By far the fastest simulator of the CPU, MMU, and memory system of an SGI multiprocessor is an SGI multiprocessor. In other words, when

. . . 148 9.4.2 Guest graphics and mouse driver setup in depth . . . . . . . . . . . . . 148 9.5 CPU hot-plugging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 9.6 PCI passthrough guests . . . . . . . . . . . . . . . . . . 185 6 Contents 12.3.6 Windows guests may cause a high CPU load . . . . . . . . . . . . . . . 185 12.3.7 Long delays when accessing shared folders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 12.4.1 Linux guests may cause a high CPU load . . . . . . . . . . . . . . . . . 187 12.4.2 AMD Barcelona CPUs . . . . . . . . . . . . . . .

. . . 157 9.4.2 Guest graphics and mouse driver setup in depth . . . . . . . . . . . . . 157 9.5 CPU hot-plugging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 9.6 PCI passthrough Windows Vista guests . . . . . . . . . . . . . . . . . . 206 12.3.6 Windows guests may cause a high CPU load . . . . . . . . . . . . . . . 206 12.3.7 Long delays when accessing shared folders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 12.4.1 Linux guests may cause a high CPU load . . . . . . . . . . . . . . . . . 208 12.4.2 AMD Barcelona CPUs . . . . . . . . . . . . . . .

resolution) Negligible overhead (a few nanoseconds per zone) Cross-platform (Windows, Linux, macOS, iOS, Android, WASM*) Hybrid profiling capabilities (sampling and/or instrumentation) (CPU and GPU instrumentation) (analyze application while it is running) Precise measurements (nanosecond resolution) Negligible overhead (a few nanoseconds per zone) Cross-platform (Windows, Linux, macOS, iOS, Android, WASM*) Hybrid (analyze application while it is running) Precise measurements (nanosecond resolution) Negligible overhead (a few nanoseconds per zone) Cross-platform (Windows, Linux, macOS, iOS, Android, WASM*) Hybrid

resolution) Negligible overhead (a few nanoseconds per zone) Cross-platform (Windows, Linux, macOS, iOS, Android, WASM*) Hybrid profiling capabilities (sampling and/or instrumentation) (CPU and GPU instrumentation) (analyze application while it is running) Precise measurements (nanosecond resolution) Negligible overhead (a few nanoseconds per zone) Cross-platform (Windows, Linux, macOS, iOS, Android, WASM*) Hybrid (analyze application while it is running) Precise measurements (nanosecond resolution) Negligible overhead (a few nanoseconds per zone) Cross-platform (Windows, Linux, macOS, iOS, Android, WASM*) Hybrid

creation is CPU and memory intensive19/55 Threads - Evaluation Thread creation is CPU and memory intensive Thread switching is expensive19/55 Threads - Evaluation Thread creation is CPU and memory threads may still consume CPU cycles!19/55 Threads - Evaluation Thread creation is CPU and memory intensive Thread switching is expensive Blocked threads may still consume CPU cycles! Shared data must correctly.19/55 Threads - Evaluation Thread creation is CPU and memory intensive Thread switching is expensive Blocked threads may still consume CPU cycles! Shared data must be synchronized correctly.

. . . 165 9.4.2 Guest graphics and mouse driver setup in depth . . . . . . . . . . . . . 165 9.5 CPU hot-plugging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 9.6 PCI passthrough . . . . . . . . . . 214 12.2.4 Frequency scaling effect on CPU usage . . . . . . . . . . . . . . . . . . 214 12.2.5 Inaccurate Windows CPU usage reporting . . . . . . . . . . . . . . . . . 215 12.2.6 Windows Vista guests . . . . . . . . . . . . . . . . . . 216 12.3.6 Windows guests may cause a high CPU load . . . . . . . . . . . . . . . 217 12.3.7 Long delays when accessing shared folders . . . . .