dendibakh/perf-ninja

Features

• CPU Architecture Analysis - Focuses on using hardware monitoring and profiling to identify memory-bound or computation-bound bottlenecks.
• Low-Level Hardware Optimization - Offers an educational program for optimizing software to utilize hardware features like CPU caches and SIMD.
• SIMD-Based Data Parallelism - Provides guidance on replacing scalar operations with hardware-specific vector instructions to process multiple data elements simultaneously.
• Compiler Optimization Guides - Provides an educational curriculum on using LTO, PGO, and function inlining to increase execution speed.
• SIMD Vectorization Labs - Provides practical labs for implementing vectorization and hardware intrinsics to accelerate data processing.
• Branch Misprediction Reduction - Eliminates CPU pipeline stalls by replacing unpredictable conditional branches with predicate instructions like conditional moves.
• CPU Architectures - Provides a curriculum on the internal design and operational cycles of CPUs, including pipeline stalls and TLB misses.
• CPU Pipeline Hazard Mitigation - Demonstrates reducing pipeline stalls by distributing updates across independent buffers to break serial dependencies.
• Instruction-Level Parallelism Optimizations - Teaches how to overlap multiple execution chains to reduce serialization bottlenecks and maximize instruction-level parallelism.
• Instruction Stream Interleaving - Implements the duplication and interleaving of calculation streams to maximize CPU reservation station utilization.
• SIMD Vectorizations - Teaches the use of compiler intrinsics and SIMD instructions to process multiple data elements in parallel.
• Cache Locality Optimizations - Provides guidance on improving memory access patterns through tiling and alignment to reduce cache misses.
• Compiler Optimizations - Covers the application of function inlining and advanced compiler-level optimizations to improve executable efficiency.
• Profile-Guided Optimizations - Teaches how to use execution profiling data to inform compiler decisions on function inlining and code placement.
• Conditional Branch Mitigation - Minimizes CPU pipeline stalls by replacing conditional branches with lookup tables or conditional moves.
• Cache Locality Studies - Provides a technical course on reducing cache contention and implementing loop tiling for better memory locality.
• Branchless Predicate Execution - Provides instructional material and labs on replacing unpredictable conditional branches with conditional move instructions and lookup tables.
• Link-Time Optimizations - Covers applying compiler transformations across multiple translation units to optimize cross-boundary function calls.
• Cache-Aware Memory Access - Teaches how to align data structures to cache line boundaries and implement loop tiling to minimize cache misses.
• Performance Analysis - Uses hardware monitoring tools to identify hotspots and determine if programs are bound by memory latency or computation.
• Software Bottleneck Identification - Detects and resolves core-bound, memory-bound, and frontend-bound bottlenecks using low-level optimization techniques.
• SIMD Lane Divergence Handling - Handles divergent loop iterations when processing independent data elements, such as pixels, in a SIMD fashion.
• Vector Lane Shuffling - Implements scalar-to-vector broadcasting to enable uniform arithmetic operations across all SIMD lanes.
• Vector Type Casting - Prevents arithmetic overflow by converting narrow integer types into wider types within vector registers.
• Code Execution Profilers - Maps assembly instructions back to original source code by generating performance profiles with debug information.
• Cache Line Contention Analysis - Identifies shared cache lines and hotspots using profiling tools to detect synchronization overhead.
• Memory Order Violation Detection - Analyzes how consecutive identical memory accesses force CPU pipeline serialization during out-of-order execution.
• Instruction Parallelism Interleaving - Instructs on breaking serialization bottlenecks by overlapping independent calculation streams.
• CPU Dependency Chain Analysis - Analyzes loop iterations to determine if calculations rely on previous results, preventing parallel instruction execution.
• Data Structure Packing - Decreases memory bandwidth usage by eliminating compiler padding and downsizing data types to shrink structure sizes.
• Huge Page Optimizations - Explains the use of large memory pages to reduce Translation Lookaside Buffer misses and improve memory throughput.
• SIMD String Processing - Includes practical exercises on using vector instructions to scan and compare multiple characters in a single operation.
• Vectorized String Processing - Reduces character search iterations by using vector instructions to process multiple characters simultaneously.
• Virtual Call Optimizations - Increases execution speed and reduces branch target mispredictions by grouping polymorphic objects of the same derived type.
• Function Inlining - Performance Labs eliminates function prologue and epilogue costs by replacing calls with function bodies to enable further optimizations.
• False Sharing Avoidances - Prevents CPU performance degradation by ensuring independent data objects reside on separate cache lines.
• Loop Unrolling Transformations - Utilizes manual loop expansion to process multiple dependency chain nodes per iteration and increase execution speed.
• Loop Tiling and Fusion - Reduces cache misses during column-wise memory access by splitting multi-dimensional arrays into small cache-fitting chunks.
• Loop Vectorization - Optimizes loops by widening accumulators and removing carry-flag bottlenecks to enable SIMD vectorization.
• Parallel Prefix Sum Calculators - Implements high-performance cumulative sums across vectors using logarithmic-complexity shift-and-add operations.
• Memory Alignment Utilities - Implements data structure alignment to cache line boundaries to prevent split loads and optimize SIMD performance.
• Software Prefetching - Provides instruction-level memory prefetching to reduce latency during random memory accesses.
• Memory Access Pattern Optimizers - Provides techniques for rearranging loop iterations to ensure contiguous memory traversal and improve cache efficiency.
• Benchmark Result Comparison - Provides tools to calculate speedup by comparing a solution's execution time against a baseline across multiple runs.
• Code Optimization Benchmarking - Ships automated benchmarking tools to verify that low-level optimizations achieve the intended performance improvements.
• HPC Education and Training - Educational resources for performance engineering and optimization.

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