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 make_large() mov rax, rbx mov BYTE PTR [rbx+1], 1 add rsp, 48 pop rbx ret 0 L A Z Y Negative-overhead abstraction! 86C++ Core Guidelines F.20: For “out” output values, prefer return values to output

Designing an ultra low-overhead multithreading runtime for Nim Mamy Ratsimbazafy mamy@numforge.co Weave https://github.com/mratsim/weave Hello! I am Mamy Ratsimbazafy During the day blockchain/Ethereum Hardware and software multithreading: definitions and use-cases ◇ Parallel APIs ◇ Sources of overhead and runtime design ◇ Minimum viable runtime plan in a weekend 4 Understanding the design space futures? (in Nim “Flowvar” to distinguish from IO-tasks futures) - Synchronization - Scheduling overhead - Thread-safe memory management 16 Dataflow parallelism - Alternative names - Pipeline parallelism

How and When You Should Measure CPU Overhead of eBPF Programs Bryce Kahle, Datadog October 28, 2020 Why should I profile eBPF programs? CI variance tracking Tools kernel.bpf_stats_enabled kernel cases: – Benchmarking + CI/CD – Sampling profiler in production How does it work? – Adds ~20ns of overhead per run Two ways to enable kernel eBPF stats sysctl procfs Three ways to access kernel eBPF stats

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

again in 2018. • The exception mechanism was addressed by Herb Sutter (Chair) in 2019: P0709: Zero-overhead deterministic exceptions: Throwing values • The exception mechanism was debated in Prague (last MOTIVATION 4• Part 0: Prologue: What are Exceptions? • Part I: History and Domain • Part II: Exceptions Overhead • Part III: Design Overview • Part IV: Alternatives • Part V: What’s Next? OUTLINE 5DISCLAIMERS to an implementation. In particular, it was understood that a poor design could cause run-time overhead and a significant increase in porting times.” B. Stroustrup, The Design and Evolution 15PART

conference…Java At least I didn’t say “Rust”…Compiling Java just in time Avoiding unnecessary overhead is crucial for fast compilation. In many compilers, constructing an intermediate representation method is a standard process. When compiling from Java bytecode, however, we can eliminate that overhead. The bytecodes themselves are an IR. Because they are primarily designed to be compact and to easily be used for that purpose. — 1997 (read)Compiling Java just in time Avoiding unnecessary overhead is crucial for fast compilation. In many compilers, constructing an intermediate representation

the context overhead for processors, hardware-based two- stage address translation reduces performance overhead for memory, and hardware-based interrupt injection reduces the context overhead and interrupt and write performance of NVDIMMs. However, NVDIMMs are hampered with heavy metadata management overhead and write amplification issues due to their journal synchronization mechanism. The EulerFS file technology, which employs pointer-based dual directory views to reduce the metadata synchronization overhead. EulerFS improves the performance of system calls including create, unlink, mkdir, and rmdir.

(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 (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

This is frequently a problem for applications dealing with timestamped data. • More administrative overhead to load balance the cluster. An ordered partitioner requires administrators to manually calculate physical disks: raw_capacity = disk_size * number_of_disks Account for file system formatting overhead (roughly 10 percent) and the RAID level you are using. For example, if using RAID-10, the calculation systems, the size of your raw data will be larger once it is loaded into Cassandra due to storage overhead. On average, raw data will be about 2 times larger on disk after it is loaded into the database

(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 (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