- Estimating Maximum Possible Perf
- Instruction Level Parallelism (ILP)
- computer architecture
- operating systems
Performance matters, it unlocks new applications, important for business python -> avx extensions: x60,000 in one example Measurement is really important and hard. CPU can overclock for a little bit. Try to control the environment
Use statistical tests to determine if real change. student t for example Plot your benchmark data. Bimodal? Two different behaviors are happening microbenchmarks: be careful. Is it inlining a bunch of stuff? Anything except your exact final application and environment is a proxy. That the proxy at all represents the real behavior is fishy. Never forget that. System clock and system counters.
Reduce data dependencies a[i++] may be faster than a[++i] because of a data dependency reduction bool in C++ outputs 0/1 but may have come from a source that didn’t. This means it needs branching code for simple satuff short circuiting && ||, try to short circuit early
Estimating Maximum Possible Perf
If compute bound: Single core freq ~3Ghz * 8 byte words -> 24Gb/s RAM SSD speeds - look it up. sequential vs no nsequential v different. Maybe ~1 gb/s as a order of magnitude
Instruction Level Parallelism (ILP)
https://arxiv.org/abs/2306.00229 Minotaur: A SIMD-Oriented Synthesizing Superoptimizer
https://dl.acm.org/doi/10.1145/3372297.3423352 HACLxN: Verified Generic SIMD Crypto (for all your favourite platforms)
What this? https://branchfree.org/2019/02/25/paper-parsing-gigabytes-of-json-per-second/ https://news.ycombinator.com/item?id=24069530 roaring bitmaps simdjson judy arrays People are mentioned warming up the branch predictors on purpose somehow Branchless programming
See also note on memory-management
dhat check for memory allocation sites that are worst https://en.wikipedia.org/wiki/Memory_pool https://en.wikipedia.org/wiki/Object_pool_pattern Can use vector to store fixed size chunks. Your own private malloc specialized for one size https://en.wikipedia.org/wiki/Slab_allocation
cp algorithms competitive programming algorithsm
really cool blog posts https://mazzo.li/archive.html
The Art of Writing Efficient Programs: An advanced programmer’s guide to efficient hardware utilization and compiler optimizations using C++ examples - Pikus
Given the potential for straightline speculation w/ deleterious performance impact, does it makes sense to align functions with speculation blocking instructions like INT3 instead of nops? microbenchmarks of return address prediction (ras)
programming parallel computers course
asm("# foo"); nice trick. Inject comment into assembly
fread vs mmap Rough advice: fread is simple and often comparable to mmap (system dependent). mmap can sometimes be up to 4x faster, use madvise, weird exceptions/signals need to be handled.
unaligned vector load + length-driven PSHUFB. What’s everyone’s favourite way to handle page crossings? overreading for short variables possibly into out of bounds memory? pshufb
CLMUL fast instruction set for galois field calculations. carryless
OSACA an analyzer of assembly code. It is on godbolt
This gruop has a number of interesting tools. https://github.com/RRZE-HPC It scrapes info from
- kerncraft loop kernel analysis and performance modelling
List of interesting optimizers - These are compiler optimizations, so hopefully your compiler does them for you, but maybe it doesn’t and maybe
https://twitter.com/lemire/status/1461181871841320962?s=20 Lemire converting integerrs to fix digit representations By considering data dependencies and using lookup tables take from 25ns to 2ns.
https://news.ycombinator.com/item?id=29107147 https://randomascii.wordpress.com/2012/12/29/the-surprising-subtleties-of-zeroing-a-register/ surprising subtleites of zeroing a register.
https://www.agner.org/optimize/ agner fog optimization manuals
https://twitter.com/nadavrot/status/1464364562409422852?s=20 memset and memcpy ooptimizations
https://twitter.com/PieCalculus/status/1464252793225678850?s=20 Go does not need a garbage collector. Compares and contrasts java GC with others. Claims Java poorly designed make high pressure on GC
https://www.intel.com/content/www/us/en/developer/articles/technical/intel-sdm.html https://www.intel.com/content/dam/www/public/us/en/documents/manuals/64-ia-32-architectures-optimization-manual.pdf intel opimization manual
https://twitter.com/pervognsen/status/1455409607426207744?s=20 mimalloc- de moura, daan leijen, ben zorn https://lobste.rs/s/4awecj/mimalloc_free_list_sharding_action https://github.com/microsoft/snmalloc/blob/c5b65d07b8079b22eec9f78bec197ea7a0fd15f2/difference.md
I feel like most algorithms and data structures are os ordinary they are kind of boring?
Sparse Sets - knuth - bitvectors + Bitvectors http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.681.8766&rep=rep1&type=pdf ullmann bitvector algos for binary constraint and subgraph iso.
Sorting algorithms Hash tables Dynamic programming Tries Graph algorithsm - shortest path, spanning tree
https://news.ycombinator.com/item?id=26590234#26592091 hash table in C. some interesting commments too linear search - an assoc list but he kept it in an array http://burtleburtle.net/bob/hash/doobs.html - hashing from z3 source code https://craftinginterpreters.com/hash-tables.html
lkinear probing vs linked list in hash table.
concurrent hash map
https://algorithmica.org/en/eytzinger https://news.ycombinator.com/item?id=26695694 Interesting. Cache-oblivious binary search. Uses the “Heap” ordering or what have you Plus a branchless comparator? I think also a big point is How do you even know when cache something is a problem. How do you use feedback and self correct? How do you organize tight loops? “smart” ways of keeping structure.
microbenchmarking performance counters - cache misses, TLB ht/miss, mispredicted branches nanobench https://arxiv.org/pdf/1911.03282.pdf VTune, perf, PAPI, libpfc,
What every programmer should know abouyt memory https://people.freebsd.org/~lstewart/articles/cpumemory.pdf
modern microprcessor 90 minute guide http://www.lighterra.com/papers/modernmicroprocessors/
https://en.wikipedia.org/wiki/Program_optimization Bentley Writing Efficient Program
https://news.ycombinator.com/item?id=28955461 - a rust optimization story https://pvk.ca/Blog/2012/07/03/binary-search-star-eliminates-star-branch-mispredictions/ https://dirtyhandscoding.wordpress.com/2017/08/25/performance-comparison-linear-search-vs-binary-search/ https://www.youtube.com/watch?v=1tEqsQ55-8I&ab_channel=MollyRocket - handmade hero guy talkin about optimizations https://www.youtube.com/watch?v=pgoetgxecw8&ab_channel=MollyRocket - refterm optimization talk. this is fascinating
- optimization - measuring.
- non-pessimization - don’t do unnecessary work
- fake optimziation - people just repeatin shit
https://www.uops.info/ https://uica.uops.info/ uica online demo gives info on what’s hurtin ya. Cycle counts and stuff microp_ops. Ports? Queue? DaY 112 of hnadmade hero. perf counter. simd. converting to simd. measuring port usage with iaca
perf seems balla. Works on ocaml btw https://ocaml.org/learn/tutorials/performance_and_profiling.html https://www.brendangregg.com/perf.html https://www.youtube.com/watch?v=fhBHvsi0Ql0&ab_channel=USENIX - linux systems performance
https://www.gem5.org/ The gem5 simulator is a modular platform for computer-system architecture research, encompassing system-level architecture as well as processor microarchitecture https://ieeexplore.ieee.org/document/8718630?denied= gem5, MARSS×86 , Multi2Sim, PTLsim, Sniper, and ZSim. gem5 as an alternaitve to qemu? http://www.diva-portal.org/smash/get/diva2:1058030/FULLTEXT01.pdf
NUMA - non uniform memory access
l1 cache. instruction and data. instruction is one way
lstopo --no-io tells you how your computer looks
large /huge pages. faster for TLB. Hugetablefs is linux suppotrt?
Transparent Huge Pages-
madvise is a call yes I’d like huge tables.
cache lines - 64 bytes. even if you read/write 1 byte your’re writing 64
M exculsively own and dirty, E exlucsive and clean, S shared, , I invalid
_builtin_prefecth. linear access is good
splitting into revcord of arrays tends to be better for cache if only using one field. compressed memory is worth it. compuitayion is fast. memory is slow. Array of structs vs struct of arrays. Compressed pointers? https://en.wikipedia.org/wiki/Tagged_pointer https://v8.dev/blog/pointer-compression
isolcpus boot time option. pinning of thread or memory to cpu
taskset. linux admin styuff. isolate cpus to certain tasks
loop stream decoder
branch predictor, pipelikne stall or bubble.
branch target predcitro
ports, execution units. some logic, some airthmetic.
perf - interrogate counters.
record report annotate stat
skid - bad - precision knobs :p :pp :ppp perf record -b perf record –call-graph lbr -j any_call,any_ret program -e intel_pt//u
LBR - last branch record - linux weekly https://lwn.net/Articles/680985/ intel processors record control flow
Intel processor trace
IPC - intrcutions per cycle. 4 is maximum ish. less than 1 is perf stat
performance ocunters -
TMAM top down microarctecture analsyis method
toplev go throgyh process. and kleen. fancy frontend to perf/
profile guided optimization may do builtin expect for you
loop alginment - 32 bit boundaries. straight from uop cache. llvm flag. align-all-nofallthru-blocks align-all-function
https://easyperf.net/blog/2018/01/18/Code_alignment_issues code alignment can changed your perfoamnce.
BOLT - vinary optimization layout tranformer. defrag your code. Puts hot code in same memory location at runtime
Daniel Lemire - simd parser. mechnisms for avodiing branching. masking operations.
Summary - cache aligned / cache aware data structures. B-trees. Compress data. Avoid random memory access. Huge pages can help. 10% speedup by enabling maybe. libnuma source memory. branch0free and lock-free. perf /toplev. Use vectorization where you can.
https://alblue.bandlem.com/ his blog
Blog links neato: https://easyperf.net/notes/ https://epickrram.blogspot.com/ https://lemire.me/blog/ http://psy-lob-saw.blogspot.com/ https://richardstartin.github.io/ https://travisdowns.github.io/ https://www.agner.org/optimize/ https://www.real-logic.co.uk/ https://groups.google.com/g/mechanical-sympathy