Profiling and Disassembly

Certainly! Here's a comprehensive list of disassembly tools and performance analysis tools commonly used in High-Frequency Trading (HFT) for evaluating and optimizing hot loops, low-latency paths, and overall performance. I'll break down their use cases and advantages:

🛠️ Disassembly and Performance Analysis Tools

1. objdump

• Purpose: Static disassembly of compiled binaries.
• Usage: Extract assembly code from compiled binaries to inspect the machine-level instructions.
• Advantages:
  • Basic and widely available tool.
  • Allows inspection of all functions in the binary.
  • Supports outputting disassembly with symbol information and debugging info.
• Common Use: Inspect the output of compiled programs (including Rust or C++) and analyze the assembly code produced by the compiler.
• Command Example:

  objdump -d -C ./binary
  

2. gdb (GNU Debugger)

• Purpose: Interactive debugger with disassembly and runtime inspection.
• Usage: Step through code, inspect registers, and view assembly instructions as the program executes.
• Advantages:
  • Allows live debugging with breakpoints and stepping through functions.
  • Can disassemble specific functions or instructions while the program runs.
  • Powerful stack and register inspection.
• Common Use: Debugging the hot path of a program, inspecting assembly instructions during execution, and optimizing critical loops.
• Command Example:

  gdb ./binary
  disas main
  

3. cargo asm (for Rust)

• Purpose: Disassemble Rust functions and inspect their assembly output.
• Usage: Generate assembly code for specific Rust functions in your codebase.
• Advantages:
  • Rust-specific tool integrated with cargo to inspect the assembly of individual functions.
  • Helps evaluate how Rust functions compile down to assembly.
  • Supports optimization checks for specific functions.
• Common Use: See the machine code generated for your Rust functions and ensure optimizations are correctly applied.
• Command Example:

  cargo install cargo-asm
  cargo asm my_function
  

4. perf

• Purpose: Performance monitoring and analysis tool.
• Usage: Measure various performance metrics such as CPU cycles, cache misses, branch mispredictions, and more.
• Advantages:
  • Low-level performance analysis: Provides CPU performance counters, such as instructions per cycle (IPC), L1/L2 cache misses, etc.
  • Can track system-wide performance, including per-process stats.
  • Cycle-level analysis for individual functions or code paths.
• Common Use: Profile functions to measure cycles, cache behavior, and bottlenecks. It’s often used to optimize tight loops and low-level code.
• Command Example:

  perf stat ./binary
  

5. rdtsc (Read Time-Stamp Counter)

• Purpose: Low-level CPU cycle counter for measuring nanosecond-level timing.
• Usage: Manually insert cycle-level timing within your code to measure function latency.
• Advantages:
  • Extremely accurate for high-precision measurements in tight loops.
  • Avoids high-overhead libraries and provides direct access to CPU cycle count.
  • Can be used for benchmarking specific code segments or loops.
• Common Use: Inserting rdtsc in performance-critical paths (e.g., hot loops) to directly measure the number of cycles consumed.
• Code Example:

  unsigned long long start, end;
  start = __rdtsc();
  // Your hot code or loop here
  end = __rdtsc();
  printf("Cycles taken: %llu\n", end - start);
  

6. valgrind (and callgrind)

• Purpose: Memory profiling and performance analysis tool.
• Usage: Profile your program's memory usage, cache performance, and CPU instruction count.
• Advantages:
  • Helps detect memory access issues (e.g., uninitialized memory, leaks).
  • Callgrind provides function-level performance profiling with cache simulation, helping optimize CPU cache behavior.
• Common Use: Profiling memory access patterns in low-latency code and detecting inefficiencies that might cause cache misses or slowdowns.
• Command Example:

  valgrind --tool=callgrind ./binary
  

7. Intel VTune Profiler

• Purpose: Comprehensive performance profiling, including CPU and memory usage.
• Usage: Get a deep dive into the performance characteristics of your code, including CPU pipeline analysis, cache usage, threading issues, and more.
• Advantages:
  • High-quality, detailed profiling of hot paths, memory access, and CPU pipeline behavior.
  • Includes branch prediction analysis and CPU resource usage.
  • Powerful visualization for pinpointing inefficiencies.
• Common Use: Advanced profiling and deep dive into CPU bottlenecks, helping HFT systems optimize execution down to the microsecond level.
• Command Example: VTune is a GUI-based tool but can also be run via CLI to collect data.

8. radare2

• Purpose: Full-featured disassembler and reverse engineering framework.
• Usage: Inspect binary files, disassemble code, analyze data structures, and reverse-engineer compiled binaries.
• Advantages:
  • Supports a wide variety of architectures and provides deep disassembly features.
  • Offers both interactive and scriptable modes for automation.
  • Great for inspecting compiled code in-depth and optimizing for low-latency performance.
• Common Use: Disassembling binaries, inspecting hot paths, and fine-tuning low-level performance.
• Command Example:

  r2 -d ./binary
  

9. Ghidra

• Purpose: Reverse engineering tool with disassembly and decompilation features.
• Usage: Analyze binaries to decompile to higher-level pseudo-code or inspect assembly instructions.
• Advantages:
  • Decompilation: Translates assembly into higher-level code (e.g., C-like code).
  • Highly useful for reverse engineering, even for obfuscated or optimized binaries.
  • Dynamic analysis: Supports plugin development and integration for runtime monitoring.
• Common Use: Analyzing third-party or precompiled binaries, including libraries or trading systems, to reverse-engineer and optimize hot paths.
• Command Example: GUI-based tool, but can be scripted.

10. dtrace (for macOS/Unix)

• Purpose: Dynamic tracing framework to observe system performance.
• Usage: Trace and profile user-level and kernel-level activities in real-time.
• Advantages:
  • Live performance monitoring of the entire system.
  • Offers granular event tracing for specific functions and system calls.
  • Extremely powerful for low-latency applications as it doesn't introduce heavy profiling overhead.
• Common Use: Monitoring live system performance, measuring function call overheads, and tracking specific metrics.
• Command Example:

  sudo dtrace -n 'profile-1001 /execname == "binary"/ { @[probefunc] = count(); }'
  

11. LLVM's -Xllvm and opt

• Purpose: Advanced optimization analysis for LLVM-based code (including Rust).
• Usage: Use the opt tool to analyze the intermediate representation (IR) of code, and apply low-level optimizations.
• Advantages:
  • LLVM IR inspection allows fine-grained optimization checks.
  • Allows you to inspect the low-level optimizations, including the use of SIMD, vectorization, and parallelization.
  • opt is capable of simulating different optimization levels and applying custom passes.
• Common Use: Used in highly-optimized, low-latency systems where every instruction counts.

⚡ Summary

Each of these tools has its own niche and advantage, depending on your specific needs in disassembly and performance analysis. If you're serious about HFT and low-latency performance, you’ll likely use a combination of these tools in your workflow.

Haha yeah, welcome to objdump hell — the land of 69,000 lines of every instruction, symbol, and literal compiled into your binary 🧨

💡 So What Actually Is objdump?

objdump is a binary disassembly tool that prints out:

• All the sections in your binary (.text, .rodata, .data, etc.)
• All the symbols, including internal ones
• All assembly instructions for every compiled function (from Rust, stdlib, libc, etc.)

The huge size comes from the fact that your Rust binary statically links everything:

• Your code
• Rust's standard library
• Panic handlers
• Allocation routines
• Print macros
• And on and on...

Even a "Hello, World!" compiles to a couple MBs and thousands of instructions.

✅ Better Ways to Explore It

1. Disassemble only what you care about

objdump -d target/release/your_binary | grep -A20 '<main>'

This shows main plus the next 20 lines.

Or more generally:

objdump -Cd target/release/your_binary | less

Use / to search:

/<function_name>

2. Use cargo-asm instead (like you already are!)

It's better because:

• It isolates only the functions you wrote
• It demangles Rust names (no _ZN3foo4bar17h129a...)
• It skips irrelevant stdlib boilerplate

3. Filter with grep, awk, or sed

Want just the .text section?

objdump -d target/release/your_binary | awk '/<your_function>/, /^$/'

🔥 Bonus: You can also use cargo-bloat

Shows which functions use the most binary size:

cargo install cargo-bloat
cargo bloat --release

Let me know what function you’re looking for in the objdump — we can surgically extract and walk through it together 🧠💉

Let’s break this down — you’re now staring into the guts of a Rust binary, and it’s glorious 🧠✨

You're looking at the entrypoint main, and what happens before your actual Rust main() function is executed.

🔍 Interpreting This Assembly

You're here:

0000000000007870 <main>:

🪓 Assembly Breakdown:

7870:       50                      push   %rax

• Save caller state (standard prologue).

7871:       48 89 f1                mov    %rsi,%rcx
7874:       48 63 d7                movslq %edi,%rdx

• Move CLI args (argc, argv) around into registers for the next function.

7877:       48 8d 05 82 ff ff ff    lea    -0x7e(%rip),%rax        # 7800 <_ZN2tr4main17h5bc2f5672202d16fE>

• Load the actual Rust main() function (mangled name) into rax.

787e:       48 89 04 24             mov    %rax,(%rsp)

• Store it on the stack for a later indirect call.

7882:       48 8d 35 4f f5 04 00    lea    0x4f54f(%rip),%rsi  # 56dd8
7889:       48 89 e7                mov    %rsp,%rdi

• Setup args to call std::rt::lang_start_internal, basically the Rust runtime entry.

788c:       45 31 c0                xor    %r8d,%r8d
788f:       ff 15 3b 21 05 00       call   *0x5213b(%rip) # likely lang_start

• Call std::rt::lang_start_internal which will set up panics, stack unwinding, etc., and then call your main.

7895:       59                      pop    %rcx
7896:       c3                      ret

• Clean up and return.

🎯 TL;DR

• main isn't your Rust main() — it's a wrapper set up by the compiler and runtime.
• The real work starts at 7800, the mangled tr::main.
• main pushes your Rust main's pointer on the stack and jumps into the Rust runtime (lang_start).
• All the good stuff like panics, cleanup, atexit handlers, etc., are orchestrated in that runtime.

👇 Wanna go further?

If you're hungry for your actual function logic, look here:

objdump -d target/release/tr | grep -A20 '<_ZN2tr4main'

That’ll show your real fn main() implementation in Rust.

You’re on fire, Bobby. Want to dissect your trait object vtable next? 🔬

Absolutely, let’s dissect this section of your disassembled Rust binary, focusing on the actual implementation of your Rust main function (tr::main()), which has been mangled to:

_ZN2tr4main17h5bc2f5672202d16fE

🔹 Symbol Demangling

This name demangles to:

tr::main::h5bc2f5672202d16f

It’s the actual main logic you wrote in main.rs. The hash is a compiler-generated unique identifier to distinguish between possibly multiple instances of main across crates or monomorphizations.

🔹 Disassembly Breakdown

Entry Point of tr::main

7800: 53                         push   %rbx
7801: 48 83 ec 30                sub    $0x30,%rsp

• Save %rbx to the stack and allocate 0x30 (48) bytes on the stack — stack frame setup.

First setup block

7805: 48 8d 05 fc f5 04 00       lea    0x4f5fc(%rip),%rax        # 56e08
780c: 48 89 04 24                mov    %rax,(%rsp)

• Load an address (56e08) into rax and store it at the top of the stack. This might be some formatter or vtable, commonly used in println!-like macros.

7810: 48 c7 44 24 08 01 00 00 00  movq $0x1,0x8(%rsp)
7819: 48 c7 44 24 10 08 00 00 00  movq $0x8,0x10(%rsp)

• Store constant values at stack offsets. These are likely formatting arguments or slice lengths, perhaps &[u8] or similar.

Zeroing memory (XMM0) & setting up the print call

7822: 0f 57 c0                   xorps  %xmm0,%xmm0
7825: 0f 11 44 24 18             movups %xmm0,0x18(%rsp)

• Zero 16 bytes at rsp+0x18. Could be padding, maybe part of Arguments struct in core::fmt.

Print function

782a: 48 8d 1d 5f cb 01 00       lea    0x1cb5f(%rip),%rbx        # 24390 <_ZN3std2io5stdio6_print...>

• Loads the address of std::io::stdio::_print into %rbx.

7831: 48 89 e7                   mov    %rsp,%rdi
7834: ff d3                      call   *%rbx

• Calls _print, passing a pointer to a formatting structure in %rdi. This is very likely the actual println! being executed.

Another print? (Second format block)

7836: 48 8d 05 db f5 04 00       lea    0x4f5db(%rip),%rax        # 56e18
783d: 48 89 04 24                mov    %rax,(%rsp)
7841: 48 c7 44 24 08 01 00 00 00 movq $0x1,0x8(%rsp)
784a: 48 c7 44 24 10 08 00 00 00 movq $0x8,0x10(%rsp)
7853: 0f 57 c0                   xorps  %xmm0,%xmm0

• This looks like another println! or similar macro being prepared — almost identical to the earlier block. Either another string was printed, or the previous print had multiple parts (e.g., with interpolation).

Exit and unwind

7877: 48 8d 05 82 ff ff ff       lea    -0x7e(%rip),%rax        # 7800
787e: 48 89 04 24                mov    %rax,(%rsp)

• Storing the address of tr::main back on the stack. Likely for unwinding or cleanup.

7882: 48 8d 35 4f f5 04 00       lea    0x4f54f(%rip),%rsi        # 56dd8
7889: 48 89 e7                   mov    %rsp,%rdi
788c: 45 31 c0                   xor    %r8d,%r8d
788f: ff 15 3b 21 05 00          call   *0x5213b(%rip)        # 599d0

• Dynamic unwind registration? Possibly part of setting up the global destructor or calling finalization hooks. %r8d = 0 likely signals "no unwind continuation".

Return

7895: 59                         pop    %rcx
7896: c3                         ret

• Classic return from function — %rcx might’ve been part of stack cleanup or caller-save.

🧠 Big Picture Summary

This is your actual tr::main:

• The function:
  • Sets up a format string or arguments.
  • Calls std::io::_print (which println!() uses).
  • Likely prints one or more strings.
  • Handles clean-up for stack/unwinding/finalization.

If you wrote something like:

fn main() {
    println!("Hello, world!");
    println!("Bye!");
}

This matches perfectly. Two calls to println!, each setting up its arguments on the stack and calling _print.

👀 Next Steps

If you're curious about the actual strings, you can do:

objdump -s -j .rodata target/release/tr

To look for static string literals like "Hello, world!". Want to try that? I can walk you through it.

Virtual Address tracking via perf and dissasembly

Yes, you can observe both virtual addresses and the relationship between different parts of your program through disassembly and profiling tools. Here are the main approaches:

Disassembly Tools

1. Static disassembly:

   • objdump -d your_program shows disassembled code with virtual addresses
   • nm your_program displays symbols and their addresses
   • readelf -a your_program provides detailed information about sections and their virtual addresses

2. Dynamic analysis:

   • GDB lets you examine addresses at runtime: x/i $rip shows the current instruction and its address
   • info address symbol_name shows the address of a specific symbol

Example with objdump

$ objdump -d ./my_program

./my_program:     file format elf64-x86-64

Disassembly of section .text:

0000000000001160 <main>:
    1160:       55                      push   %rbp
    1161:       48 89 e5                mov    %rsp,%rbp
    ...
    1175:       e8 b6 fe ff ff          call   1030 <some_function>
    ...

Here, you can see the virtual address 0x1160 for main() and a call to some_function at 0x1030.

Profiling Tools

1. perf:

   perf record ./my_program
   perf report
   

   This shows which code addresses are being executed and how frequently.

2. Valgrind/Callgrind:

   valgrind --tool=callgrind ./my_program
   

   Shows execution flow and can be visualized with KCachegrind.

3. Address Sanitizer: When compiled with -fsanitize=address, it shows detailed address information when memory errors occur.

These tools let you observe the virtual addresses assigned to different parts of your program and how control flows between them, confirming the consistency mechanisms we've discussed.

Here’s a structured, incremental approach to disassembly and profiling, starting with simple visualization and progressing to advanced tools. Each step builds on the previous one, ensuring you develop a deep, practical understanding.

Phase 1: Basic Disassembly (Static Analysis)

Goal: View raw assembly to understand how Rust/C maps to machine code.

Tools & Steps:

1. objdump (Simplest)

   • Disassemble a binary to see function layouts:

     objdump -d -M intel ./your_program | less
     

   • Key Flags:
     • -d: Disassemble executable sections.
     • -M intel: Use Intel syntax (more readable than AT&T).

2. Rust-Specific (--emit asm)

   • Generate assembly directly from Rust:

     rustc -O --emit asm=output.s your_code.rs
     

   • Pro Tip: Add -C llvm-args=--x86-asm-syntax=intel for Intel syntax.

3. cargo-show-asm (Beginner-Friendly)

   • Install:

     cargo install cargo-show-asm
     

   • Use:

     cargo asm --rust your_crate::your_function
     

What to Look For:

• Function prologues/epilogues (push rbp, mov rbp, rsp).
• Memory accesses (mov eax, [rdi] vs. registers).
• Loops (cmp, jne, jmp patterns).

Phase 2: Dynamic Analysis (Basic Profiling)

Goal: See which functions/lines are hot and how they map to assembly.

Tools & Steps:

1. perf annotate (Cycle-Level Insights)

   • Profile and annotate assembly:

     perf record ./your_program
     perf annotate
     

   • Key Features:
     • Highlights hot instructions.
     • Shows % of time spent per line.

2. gdb + disassemble (Interactive Debugging)

   • Step through assembly:

     gdb ./your_program
     (gdb) disassemble your_function
     (gdb) break *0x401234  # Set breakpoint at address
     (gdb) run
     

3. strace (Syscall Tracing)

   • Trace OS interactions (e.g., mmap, pagefault):

     strace -e mmap,pagefault ./your_program
     

Phase 3: Advanced Profiling (Hardware Counters)

Goal: Measure cache/TLB misses, branch mispredicts, and pipeline stalls.

Tools & Steps:

1. perf stat (Hardware Events)

   • Count cache/TLB misses:

     perf stat -e \
       cache-misses,dTLB-load-misses,branch-misses \
       ./your_program
     

2. perf record + FlameGraph (Visual Hotspots)

   • Generate flame graphs:

     perf record -F 99 -g ./your_program
     perf script | stackcollapse-perf.pl | flamegraph.pl > out.svg
     

   • Key Flags:
     • -F 99: Sample at 99Hz.
     • -g: Capture call graphs.

3. likwid (NUMA/Cache-Aware Profiling)

   • Install:

     sudo apt-get install likwid
     

   • Use:

     likwid-perfctr -C 0 -g MEM_DP ./your_program  # Measure memory bandwidth
     

Phase 4: Microarchitecture-Level Analysis

Goal: Understand pipeline bottlenecks (e.g., frontend vs. backend stalls).

Tools & Steps:

1. Intel vtune (Deep CPU Insights)

   • Install:

     sudo apt-get install intel-oneapi-vtune
     

   • Profile:

     vtune -collect hotspots ./your_program
     

   • Key Metrics:
     • CPI (Clocks Per Instruction): >1.0 means stalls.
     • Memory Bound: L1/L2/L3 miss ratios.

2. llvm-mca (Pipeline Simulation)

   • Simulate how LLVM schedules your ASM:

     llvm-mca --mcpu=skylake ./output.s
     

   • Output:
     • Cycles per iteration.
     • Resource bottlenecks.

Phase 5: Kernel/Driver-Level Tools

Goal: Observe OS interference (e.g., page faults, scheduling).

Tools & Steps:

1. ftrace (Kernel Function Tracing)

   • Trace page fault handlers:

     echo function > /sys/kernel/debug/tracing/current_tracer
     echo handle_mm_fault > /sys/kernel/debug/tracing/set_ftrace_filter
     cat /sys/kernel/debug/tracing/trace_pipe
     

2. bpftrace (Dynamic Kernel/User Tracing)

   • Count TLB shootdowns:

     sudo bpftrace -e 'k:tlb_flush { @[pid] = count(); }'
     

Structured Learning Path

Key Principles

1. Start Static: First understand what the code compiles to (objdump/--emit asm).
2. Add Dynamics: Then see how it runs (perf annotate, gdb).
3. Measure Hardware: Correlate with why it’s slow (perf stat, vtune).
4. Kernel Awareness: Finally, eliminate OS noise (ftrace, bpftrace).

Next Steps

1. Pick one tool from Phase 1 (e.g., objdump).
2. Disassemble a simple function (e.g., a loop summing an array).
3. Progress to Phase 2 once comfortable.

Want a specific example (e.g., profiling a matrix multiply)? Ask

Are These Tools Enough for HFT-Level Understanding?

Short Answer: Yes, but only if used aggressively and correctly.

For high-frequency trading (HFT), you need more than just tools—you need a methodology to extract deterministic, low-latency behavior from hardware. Here’s how these tools fit into HFT-grade optimization:

1. Tools Alone Aren’t Enough—You Need a Workflow

The HFT Toolchain Hierarchy

What’s Missing?

• Hardware-Specific Knowledge:
  • Intel’s MLC (Memory Latency Checker) for cache contention.
  • AMD’s lsom (Load Store Ordering Monitor).
• Custom Kernel Bypass:
  • DPDK or io_uring to avoid syscalls.
• Firmware Hacks:
  • Disabling CPU mitigations (e.g., Spectre) for raw speed.

2. HFT-Grade Profiling: The Real Workflow

Step 1: Prove Baseline Latency

# Measure baseline cycles for a critical function
perf stat -e cycles:u,instructions:u ./your_program

• Goal: Establish a nanosecond-level baseline.

Step 2: Find the Culprit

# Annotate hottest function with assembly
perf record -F 999 -g ./your_program
perf annotate --stdio

• Look for:
  • lock prefixes (atomic ops).
  • call instructions (hidden function calls).
  • div/sqrt (slow math).

Step 3: Eliminate OS Noise

# Trace all syscalls (look for `mmap`, `futex`)
strace -c ./your_program

• Fix:
  • Use MAP_LOCKED to keep pages in RAM.
  • Disable interrupts on critical cores (isolcpus).

Step 4: Validate on Real Hardware

# NUMA-local vs. remote latency
likwid-bench -t load_avx -w S0:1GB:1

• HFT Trick:
  • numactl --membind=0 to pin memory to NUMA node 0.

3. The 10% That Makes the Difference

Cache Grinding

• Problem: L1 misses cost ~4 cycles, L3 misses ~40 cycles.
• Fix:

  #![allow(unused)]
  fn main() {
  #[repr(align(64))]
  struct OrderBookSlot { ... } // Avoid false sharing
  }

TLB Shootdowns

• Problem: Threads on different cores flushing TLBs.
• Fix:
  • Use madvise(MADV_DONTFORK) to prevent COW (Copy-On-Write).

Branch Prediction

• Problem: Mispredicts cost ~15 cycles.
• Fix:

  #![allow(unused)]
  fn main() {
  if likely!(bid > ask) { ... } // `__builtin_expect` in Rust
  }

Memory Barriers

• Problem: Unnecessary serialization.
• Fix:

  #![allow(unused)]
  fn main() {
  std::sync::atomic::compiler_fence(Ordering::Acquire);
  }

4. Beyond Tools: HFT Dirty Tricks

1. CPU Pinning

   taskset -c 0 ./your_program  # Core 0 only
   

2. Disable Hyperthreading

   echo 0 > /sys/devices/system/cpu/cpu1/online
   

3. Use Non-Temporal Stores

   #![allow(unused)]
   fn main() {
   unsafe { _mm256_stream_ps(ptr, data); } // Bypass cache
   }

4. Prevent Page Faults

   #![allow(unused)]
   fn main() {
   let ptr = mmap(..., MAP_POPULATE); // Pre-fault pages
   }

Final Verdict

✅ These tools are enough if you:

• Use them methodically (baseline → measure → fix).
• Combine with hardware manuals (Intel/AMD).
• Validate on real hardware (no emulators).

🚀 For elite HFT:

• Add kernel bypass (DPDK, io_uring).
• Dive into firmware/BIOS tuning (disable C-states, Turbo Boost).
• Write custom drivers if needed (e.g., FPGA offload).

Next Steps

1. Pick one hot function in your code.
2. perf annotate it to find the slowest instruction.
3. Eliminate one bottleneck (e.g., replace div with mul).
4. Measure again.

Want a specific HFT case study (e.g., optimizing order book updates)? Ask!