portlandhodl

Notes about Sparse Merkle Trees

• The primary issue with a SMT is the calculation between each leaf and their intersection with other leaves.
• A tree with a single sparse element is very easy as you just can add that element in an calculate the path up the tree.
• When you have multiple keys then you need to make sure when you calculate the path up the tree you need to consider the impact other leaves have on your path to get to the root.

portlandhodl

#include <iostream>
#include <chrono>
#include <openssl/sha.h>
#include <cpuid.h>

bool check_sha_support() {
    unsigned int eax, ebx, ecx, edx;
    if (__get_cpuid(7, &eax, &ebx, &ecx, &edx)) {
        return (ebx & (1 << 29)) != 0;  // Check bit 29 for SHA extensions
    }

portlandhodl

use actix_web::{get, web, App, HttpResponse, HttpServer, Responder};
use bitcoincore_rpc::{Auth, Client, RpcApi};
use serde_json::Value;

struct AppState {
    btc_client: Client,
}

#[get("/blocktemplate")]
async fn get_block_template(data: web::Data<AppState>) -> impl Responder {

portlandhodl

#!/bin/bash

# Update the system
sudo yum update -y

# Install Docker
sudo amazon-linux-extras install docker -y
sudo systemctl start docker
sudo systemctl enable docker
sudo usermod -aG docker ec2-user

portlandhodl

import requests
import csv
from datetime import datetime
import time
import matplotlib.pyplot as plt
import pandas as pd

# Bitcoin node RPC connection details
rpc_user = 'your_rpc_username'
rpc_password = 'your_rpc_password'

portlandhodl

import requests
import csv
from datetime import datetime
import time
import matplotlib.pyplot as plt
import pandas as pd

# Bitcoin node RPC connection details
rpc_user = 'your_rpc_username'
rpc_password = 'your_rpc_password'

portlandhodl

package main

import (
	"encoding/json"
	"fmt"
	"io"
	"net/http"
)

type SystemInfo struct {

portlandhodl

clang++ -x cuda --cuda-gpu-arch=sm_61 -I/opt/cuda/lib64 axpy.cu -o axpy -L/opt/cuda/lib32 -lcudart_static -ldl -lrt -pthread

clang++: warning: CUDA version is newer than the latest supported version 12.3 [-Wunknown-cuda-version]
In file included from <built-in>:1:
In file included from /usr/local/lib/clang/18/include/__clang_cuda_runtime_wrapper.h:41:
In file included from /usr/local/lib/clang/18/include/cuda_wrappers/cmath:27:
In file included from /usr/lib64/gcc/x86_64-pc-linux-gnu/14.1.1/../../../../include/c++/14.1.1/cmath:49:
/usr/lib64/gcc/x86_64-pc-linux-gnu/14.1.1/../../../../include/c++/14.1.1/bits/std_abs.h:137:7: error: __float128 is not supported on this target
  137 |   abs(__float128 __x)
      |       ^

portlandhodl

#include <iostream>

__global__ void axpy(float a, float* x, float* y) {
  y[threadIdx.x] = a * x[threadIdx.x];
}

int main(int argc, char* argv[]) {
  const int kDataLen = 4;

  float a = 2.0f;

portlandhodl

###
# Portland.HODL USB HID Latency Tester
# Engineered for RaspberryPi(4/5)

import csv
import hid
import time
import RPi.GPIO as GPIO

def list_hid_devices():