Priority queue parallel job processing done!

Not too bad since I worked it out in Java. Good practice of heapq module in Python! A lot of fun :)

1 files changed, 47 insertions, 0 deletions

diff --git a/sources/job_queue.py b/sources/job_queue.py
new file mode 100644
index 0000000..8928d4a
--- /dev/null
+++ b/sources/job_queue.py
@@ -0,0 +1,47 @@
+# python3
+
+from heapq import heappush, heappop
+from collections import namedtuple
+
+AssignedJob = namedtuple("AssignedJob", ["worker", "started_at"])
+
+
+def assign_jobs(n_workers, jobs):
+    # Naive algorithm
+    result = []
+    next_free_time = [0] * n_workers
+    for job in jobs:
+        next_worker = min(range(n_workers), key=lambda w: next_free_time[w])
+        result.append(AssignedJob(next_worker, next_free_time[next_worker]))
+        next_free_time[next_worker] += job
+
+    return result
+
+
+def assign_jobs_improved(n_workers, jobs):
+    result = []
+    worker_priority_q = []
+    for i in range(n_workers):
+        heappush(worker_priority_q, (0, i))
+
+    for j in range(len(jobs)):
+        pair = heappop(worker_priority_q)
+        result.append(AssignedJob(pair[1], pair[0]))
+        heappush(worker_priority_q, (pair[0] + jobs[j], pair[1]))
+
+    return result
+
+
+def main():
+    n_workers, n_jobs = map(int, input().split())
+    jobs = list(map(int, input().split()))
+    assert len(jobs) == n_jobs
+
+    assigned_jobs = assign_jobs_improved(n_workers, jobs)
+
+    for job in assigned_jobs:
+        print(job.worker, job.started_at)
+
+
+if __name__ == "__main__":
+    main()