gparallel is a GPU-aware parallel job scheduler with a ** tmux-like TUI** for managing GPU workloads on single machines. Perfect for researchers and ML engineers who need to maximize GPU utilization without the complexity of cluster managers.

• 🚀 Automatic GPU allocation - Jobs distributed across available GPUs
• 📊 Real-time TUI - Beautiful terminal interface with GPU status, job queue, and live logs
• 📜 Smart scrolling - Navigate through large job queues with arrow keys
• 🔄 Job state tracking - Visual indicators for QUEUE, RUN, DONE, and FAIL states
• 💾 GPU memory monitoring - Real-time memory usage with color-coded indicators
• 🎯 GPU status indicators - ● (running) / ○ (idle) status for each GPU
• ⚡ Non-blocking execution - Jobs start immediately as GPUs become available
• 🛑 Graceful shutdown - Ctrl+C kills all running jobs cleanly

# Create a command list (one command per line) cat > commands.txt <<'EOF' python train.py --model bert --epochs 10 python train.py --model gpt2 --epochs 20 python eval.py --checkpoint model_best.pt EOF # Run with TUI (default) gparallel commands.txt # Run without TUI for CI/scripting gparallel commands.txt --no-tui

When running in TUI mode, gparallel displays a comprehensive interface:

┌─ GPUs ───────────────────┐┌─ Job queue ──────────────────────────────────┐ │0 ● RTX4090 20312 MB ││3c93a4f1 train.py --model bert RUN G0 │ │1 ○ RTX4090 24576 MB ││[f1ed8a92 train.py --model gpt2 QUEUE] │ │2 ● RTX4090 18432 MB ││a8b2c3d4 eval.py --checkpoint... RUN G2 │ └──────────────────────────┘└──────────────────────────────────────────────┘ ┌─ Live log : job #f1ed8a92 (tail -f) ─────────────────────────────────┐ │No logs yet for job f1ed8a92 (train.py --model gpt2) │ │ │ └───────────────────────────────────────────────────────────────────────┘ ↑/↓ Navigate jobs q Quit (jobs continue) Ctrl+C Force quit & stop all jobs Auto-exit when all jobs complete 

1. GPU Panel (top-left)

   • GPU ID and name
   • Status indicator: ● (running job) / ○ (idle)
   • Available memory in MB with color coding:
     • 🟢 Green: <50% usage
     • 🟡 Yellow: 50-80% usage
     • 🔴 Red: >80% usage

2. Job Queue Panel (top-right)

   • Job ID (first 8 chars of UUID)
   • Command (truncated if too long)
   • State: QUEUE, RUN (with GPU), DONE, or FAIL
   • Scrollable with ↑/↓ keys when many jobs exist

3. Live Log Panel (bottom)

   • Shows stdout/stderr from selected job
   • Auto-selects first job
   • Updates in real-time
   • Limited to last 1000 lines per job

• ↑/↓ - Navigate through jobs in the queue
• q - Quit gparallel (jobs continue running in background)
• Ctrl+C - Force quit and terminate all running jobs

# Clone the repository git clone https://github.com/yourusername/gparallel cd gparallel # Build with Cargo cargo build --release # Copy to your PATH sudo cp target/release/gparallel /usr/local/bin/

# Once published to crates.io cargo install gparallel

• Rust 1.70+
• NVIDIA drivers and CUDA toolkit
• Terminal with UTF-8 support for UI elements

# Run jobs from a file gparallel jobs.txt # Specify visible GPUs CUDA_VISIBLE_DEVICES=0,2,4 gparallel jobs.txt # Disable TUI for scripts/CI gparallel jobs.txt --no-tui

Create a text file with one command per line:

# train_jobs.txt python train.py --config configs/experiment1.yaml python train.py --config configs/experiment2.yaml python train.py --config configs/experiment3.yaml ./run_benchmark.sh --gpu-test jupyter nbconvert --execute notebook.ipynb

# Generate parameter sweep for lr in 0.01 0.001 0.0001; do for bs in 32 64 128; do echo "python train.py --lr $lr --batch-size $bs" done done > sweep_jobs.txt gparallel sweep_jobs.txt

1. GPU Detection

   • Respects CUDA_VISIBLE_DEVICES if set
   • Uses NVML for GPU information and memory monitoring
   • Falls back to nvidia-smi if NVML unavailable
   • Assumes single GPU if detection fails

2. Job Scheduling

   • Round-robin assignment to available GPUs
   • Jobs queued when all GPUs busy
   • Immediate dispatch when GPU becomes free
   • Each job gets exclusive GPU via CUDA_VISIBLE_DEVICES

3. Process Management

   • Spawns jobs via bash -c
   • Captures stdout/stderr to memory buffers
   • Tracks process IDs for signal handling
   • Updates job states in real-time

4. Memory Monitoring

   • Polls GPU memory every 2 seconds
   • Updates display with current free memory
   • Color-codes based on usage percentage

gparallel [OPTIONS] <FILENAME> Arguments: <FILENAME> File containing commands to execute (one per line) Options: --no-tui Disable TUI and use plain text output -h, --help Print help -V, --version Print version 

If gparallel shows incorrect GPUs:

# Check NVIDIA driver nvidia-smi # Force specific GPUs export CUDA_VISIBLE_DEVICES=0,1,2 gparallel jobs.txt

If TUI doesn't appear:

# Check terminal capabilities echo $TERM # Force non-TUI mode gparallel jobs.txt --no-tui

Common causes:

• All GPUs busy (check GPU panel)
• Previous job hasn't released GPU yet
• Command syntax error (check failed jobs)

Contributions welcome! Please open an issue or submit a pull request.

# Install Rust curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh # Clone and build git clone https://github.com/yourusername/gparallel cd gparallel cargo build # Run tests cargo test # Run with debug output RUST_LOG=debug cargo run -- test_jobs.txt

MIT License - see LICENSE file for details.