Sure, here's an improved version of your code that should be faster and more memory-efficient:

```

from collections import defaultdict

import multiprocessing

def process_chunk(chunk):

d = defaultdict(lambda: (0, 0.0))

for l in chunk:

v = l.split(" ")

b, i, o = int(v[0]), int(v[1]), int(v[2])

for x in range(4+i, 4+i+o):

d[str(v[x+o])]=(b, float(v[x]))

for x in range(4, 4+i):

d.pop(str(v[x]), None)

return d

def main():

NUM_PROCESSES = 4

CHUNK_SIZE = 1000000

with open('d.txt', 'r') as f:

pool = multiprocessing.Pool(processes=NUM_PROCESSES)

chunks = [f.readlines(CHUNK_SIZE) for _ in range(NUM_PROCESSES)]

results = pool.map(process_chunk, chunks)

final_dict = defaultdict(lambda: (0, 0.0))

for d in results:

for k, v in d.items():

final_dict[k] = v

print(final_dict)

if __name__ == '__main__':

main()

```

In this version, I've used a `defaultdict` instead of a regular dictionary, which allows you to specify a default value for any new keys. This eliminates the need to check if a key exists in the dictionary before updating it.

I've also used multiprocessing to split the file into chunks and process them in parallel. This can help reduce the overall processing time.

Let me know if you have any other questions!