problem_id stringlengths 1 66 | category stringclasses 3
values | statement stringlengths 0 20.2k | config stringlengths 20 1.88k |
|---|---|---|---|
cant_be_late_multi/high_availability_loose_deadline_large_overhead | research | Cant-Be-Late Multi-Region Scheduling Problem
================================
Problem Setting
---------------
You are given a long-running compute job that must complete before a fixed hard deadline.
At each time step, you must choose which AWS region to run in and which type of cloud compute resource to use:
- **Sp... | {
"dependencies": {
"uv_project": "../common/cant-be-late-simulator"
},
"datasets": [
{
"type": "local_tar",
"path": "../common/real_traces.tar.gz",
"target": "../common/cant-be-late-simulator/data",
"expected_glob": "converted_multi_region_aligned/*/0.json"
}
],
"tag": "os... |
cant_be_late_multi/high_availability_loose_deadline_small_overhead | research | Cant-Be-Late Multi-Region Scheduling Problem
================================
Problem Setting
---------------
You are given a long-running compute job that must complete before a fixed hard deadline.
At each time step, you must choose which AWS region to run in and which type of cloud compute resource to use:
- **Sp... | {
"dependencies": {
"uv_project": "../common/cant-be-late-simulator"
},
"datasets": [
{
"type": "local_tar",
"path": "../common/real_traces.tar.gz",
"target": "../common/cant-be-late-simulator/data",
"expected_glob": "converted_multi_region_aligned/*/0.json"
}
],
"tag": "os... |
cant_be_late_multi/high_availability_tight_deadline_large_overhead | research | Cant-Be-Late Multi-Region Scheduling Problem
================================
Problem Setting
---------------
You are given a long-running compute job that must complete before a fixed hard deadline.
At each time step, you must choose which AWS region to run in and which type of cloud compute resource to use:
- **Sp... | {
"dependencies": {
"uv_project": "../common/cant-be-late-simulator"
},
"datasets": [
{
"type": "local_tar",
"path": "../common/real_traces.tar.gz",
"target": "../common/cant-be-late-simulator/data",
"expected_glob": "converted_multi_region_aligned/*/0.json"
}
],
"tag": "os... |
cant_be_late_multi/high_availability_tight_deadline_small_overhead | research | Cant-Be-Late Multi-Region Scheduling Problem
================================
Problem Setting
---------------
You are given a long-running compute job that must complete before a fixed hard deadline.
At each time step, you must choose which AWS region to run in and which type of cloud compute resource to use:
- **Sp... | {
"dependencies": {
"uv_project": "../common/cant-be-late-simulator"
},
"datasets": [
{
"type": "local_tar",
"path": "../common/real_traces.tar.gz",
"target": "../common/cant-be-late-simulator/data",
"expected_glob": "converted_multi_region_aligned/*/0.json"
}
],
"tag": "os... |
cant_be_late_multi/low_availability_loose_deadline_large_overhead | research | Cant-Be-Late Multi-Region Scheduling Problem
================================
Problem Setting
---------------
You are given a long-running compute job that must complete before a fixed hard deadline.
At each time step, you must choose which AWS region to run in and which type of cloud compute resource to use:
- **Sp... | {
"dependencies": {
"uv_project": "../common/cant-be-late-simulator"
},
"datasets": [
{
"type": "local_tar",
"path": "../common/real_traces.tar.gz",
"target": "../common/cant-be-late-simulator/data",
"expected_glob": "converted_multi_region_aligned/*/0.json"
}
],
"tag": "os... |
cant_be_late_multi/low_availability_loose_deadline_small_overhead | research | Cant-Be-Late Multi-Region Scheduling Problem
================================
Problem Setting
---------------
You are given a long-running compute job that must complete before a fixed hard deadline.
At each time step, you must choose which AWS region to run in and which type of cloud compute resource to use:
- **Sp... | {
"dependencies": {
"uv_project": "../common/cant-be-late-simulator"
},
"datasets": [
{
"type": "local_tar",
"path": "../common/real_traces.tar.gz",
"target": "../common/cant-be-late-simulator/data",
"expected_glob": "converted_multi_region_aligned/*/0.json"
}
],
"tag": "os... |
cant_be_late_multi/low_availability_tight_deadline_large_overhead | research | Cant-Be-Late Multi-Region Scheduling Problem
================================
Problem Setting
---------------
You are given a long-running compute job that must complete before a fixed hard deadline.
At each time step, you must choose which AWS region to run in and which type of cloud compute resource to use:
- **Sp... | {
"dependencies": {
"uv_project": "../common/cant-be-late-simulator"
},
"datasets": [
{
"type": "local_tar",
"path": "../common/real_traces.tar.gz",
"target": "../common/cant-be-late-simulator/data",
"expected_glob": "converted_multi_region_aligned/*/0.json"
}
],
"tag": "os... |
cant_be_late_multi/low_availability_tight_deadline_small_overhead | research | Cant-Be-Late Multi-Region Scheduling Problem
================================
Problem Setting
---------------
You are given a long-running compute job that must complete before a fixed hard deadline.
At each time step, you must choose which AWS region to run in and which type of cloud compute resource to use:
- **Sp... | {
"dependencies": {
"uv_project": "../common/cant-be-late-simulator"
},
"datasets": [
{
"type": "local_tar",
"path": "../common/real_traces.tar.gz",
"target": "../common/cant-be-late-simulator/data",
"expected_glob": "converted_multi_region_aligned/*/0.json"
}
],
"tag": "os... |
cloudcast | research | Cloudcast Broadcast Optimization Problem
========================================
Problem Setting
---------------
Design broadcast topology optimization for multi-cloud data distribution. Given a source node and multiple destination nodes across AWS, Azure, and GCP, find the optimal broadcast paths that minimize trans... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"tag": "ai"
}
|
cross_entropy | research | Cross Entropy Optimization Problem
====================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for Cross Entropy loss computation on GPU. This problem focuses on implementing efficient cross entropy loss kernels using Triton's JIT compilation system.
The ch... | dependencies:
uv_project: resources
tag: hpc
runtime:
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
|
decoding_attn | research | Decoding Attention Optimization Problem
========================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for Decoding Attention computation on GPU. This problem focuses on implementing efficient attention kernels for decoder-only transformer models using Trit... | dependencies:
uv_project: resources
tag: hpc
runtime:
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
|
flash_attn | research | Flash Attention Optimization Problem
=====================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for Flash Attention computation on GPU. This problem focuses on implementing efficient attention kernels with causal masking support using Triton's JIT compilat... | dependencies:
uv_project: resources
tag: hpc
runtime:
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
|
fused_linear_ce | research | Fused Linear Cross Entropy Optimization Problem
===============================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for Fused Linear Cross Entropy loss computation on GPU. This problem focuses on implementing efficient fused kernels that combine matrix mu... | dependencies:
uv_project: resources
tag: hpc
runtime:
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
|
fused_linear_jsd | research | Fused Linear Jensen-Shannon Divergence Optimization Problem
==========================================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for Fused Linear Jensen-Shannon Divergence (JSD) computation on GPU. This problem focuses on implementing efficient ... | dependencies:
uv_project: resources
tag: hpc
runtime:
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
|
gdpa_attention | research | GDPA Attention Optimization Problem
===================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for GDPA (Gated Dot-Product Attention) computation on GPU. This problem focuses on implementing efficient attention kernels with gated Q and K tensors using Triton... | dependencies:
uv_project: resources
tag: hpc
runtime:
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
|
gemm_optimization/annoying | research | GEMM Optimization Problem
=========================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for General Matrix-Matrix Multiplication (GEMM) on GPU. This problem focuses on implementing efficient matrix multiplication kernels using Triton's JIT compilation system.
The challe... | dependencies:
uv_project: resources
datasets: []
tag: hpc
runtime:
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
|
gemm_optimization/k_skewed | research | GEMM Optimization Problem
=========================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for General Matrix-Matrix Multiplication (GEMM) on GPU. This problem focuses on implementing efficient matrix multiplication kernels using Triton's JIT compilation system.
The challe... | dependencies:
uv_project: resources
datasets: []
tag: hpc
runtime:
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
|
gemm_optimization/near_tile | research | GEMM Optimization Problem
=========================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for General Matrix-Matrix Multiplication (GEMM) on GPU. This problem focuses on implementing efficient matrix multiplication kernels using Triton's JIT compilation system.
The challe... | dependencies:
uv_project: resources
datasets: []
tag: hpc
runtime:
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
|
gemm_optimization/rectangles | research | GEMM Optimization Problem
=========================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for General Matrix-Matrix Multiplication (GEMM) on GPU. This problem focuses on implementing efficient matrix multiplication kernels using Triton's JIT compilation system.
The challe... | dependencies:
uv_project: resources
datasets: []
tag: hpc
runtime:
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
|
gemm_optimization/squares | research | GEMM Optimization Problem
=========================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for General Matrix-Matrix Multiplication (GEMM) on GPU. This problem focuses on implementing efficient matrix multiplication kernels using Triton's JIT compilation system.
The challe... | dependencies:
uv_project: resources
datasets: []
tag: hpc
runtime:
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
|
gemm_optimization/transformerish | research | GEMM Optimization Problem
=========================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for General Matrix-Matrix Multiplication (GEMM) on GPU. This problem focuses on implementing efficient matrix multiplication kernels using Triton's JIT compilation system.
The challe... | dependencies:
uv_project: resources
datasets: []
tag: hpc
runtime:
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
|
group_gemm | research | Group GEMM Optimization Problem
================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for Batched Matrix-Matrix Multiplication (BMM) on GPU. This problem focuses on implementing efficient batched matrix multiplication kernels using Triton's JIT compilation... | dependencies:
uv_project: resources
tag: hpc
runtime:
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
|
imagenet_pareto/1m | research | ImageNet Pareto Optimization - 1M Parameter Variant
===================================================
Problem Setting
---------------
Train a neural network on a synthetic ImageNet-like dataset to maximize accuracy while staying within a parameter budget of 1,000,000 parameters.
Objective: Achieve the highest possi... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"runtime": {
"timeout_seconds": 3600
},
"tag": "ai"
}
|
imagenet_pareto/200k | research | ImageNet Pareto Optimization - 200K Parameter Variant
=====================================================
Problem Setting
---------------
Train a neural network on a synthetic ImageNet-like dataset to maximize accuracy while staying within a parameter budget of 200,000 parameters.
Objective: Achieve the highest pos... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"runtime": {
"timeout_seconds": 3600
},
"tag": "ai"
}
|
imagenet_pareto/2_5m | research | ImageNet Pareto Optimization - 2.5M Parameter Variant
=====================================================
Problem Setting
---------------
Train a neural network on a synthetic ImageNet-like dataset to maximize accuracy while staying within a parameter budget of 2,500,000 parameters.
Objective: Achieve the highest p... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"runtime": {
"timeout_seconds": 3600
},
"tag": "ai"
}
|
imagenet_pareto/500k | research | ImageNet Pareto Optimization - 500K Parameter Variant
=====================================================
Problem Setting
---------------
Train a neural network on a synthetic ImageNet-like dataset to maximize accuracy while staying within a parameter budget of 500,000 parameters.
Objective: Achieve the highest pos... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"runtime": {
"timeout_seconds": 3600
},
"tag": "ai"
}
|
imagenet_pareto/5m | research | ImageNet Pareto Optimization - 5M Parameter Variant
===================================================
Problem Setting
---------------
Train a neural network on a synthetic ImageNet-like dataset to maximize accuracy while staying within a parameter budget of 5,000,000 parameters.
Objective: Achieve the highest possi... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"runtime": {
"timeout_seconds": 3600
},
"tag": "ai"
}
|
llm_router | research | LLM Router
================================
Overview
--------
This benchmark evaluates a language model's ability to implement an LLM routing policy. Given a user query, the router must choose one model from a small candidate set with different cost–quality tradeoffs. The goal is to maximize accuracy while minimizing ... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"tag": "ai"
} |
llm_sql/large | research | Problem Setting
---------------
Consider a CSV file with $N$ rows and $M$ columns, where $M \leq 10$. We feed each row to an LLM inference engine (with a prefix KV cache) by concatenating all column values in that row. For the $i$-th row with entries $A[i,1], A[i,2], \ldots, A[i,M]$, we construct the input string:
``... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"runtime": {
"timeout_seconds": 1800
},
"tag": "db"
}
|
llm_sql/small | research | Problem Setting
---------------
Consider a CSV file with $N$ rows and $M$ columns, where $M \leq 10$. We feed each row to an LLM inference engine (with a prefix KV cache) by concatenating all column values in that row. For the $i$-th row with entries $A[i,1], A[i,2], \ldots, A[i,M]$, we construct the input string:
``... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"runtime": {
"timeout_seconds": 1800
},
"tag": "db"
}
|
mamba2_scan | research | Mamba2 Scan Optimization Problem
==================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for Mamba2 scan computation on GPU. This problem focuses on implementing efficient sequential scan operations using chunked parallelism with Triton's JIT compilation s... | tag: hpc
dependencies:
uv_project: resources
runtime:
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
|
mixed_gemm | research | Mixed GEMM Optimization Problem
=================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for Mixed GEMM (Linear + Bias + GELU) computation on GPU. This problem focuses on implementing efficient fused kernels that combine matrix multiplication, bias addition,... | dependencies:
uv_project: resources
tag: hpc
runtime:
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
|
nbody_simulation/random_100k | research | N-Body Simulation Problem - 100,000 Particles
=============================================
Problem Setting
---------------
Design and optimize a high-performance parallel N-body simulation. In physics and astronomy, an N-body simulation models the dynamics of particles under gravitational forces. The available hardwa... | tag: hpc
runtime:
language: cpp
timeout_seconds: 600
environment: "C++17 with OpenMP (GCC with libgomp1) on Ubuntu 22.04, 16 vCPUs"
docker:
image: "gcc:13"
resources:
cloud: aws
instance_type: c7i.4xlarge
cpus: "16"
memory: "32"
|
nbody_simulation/random_10k | research | N-Body Simulation Problem - 10,000 Particles
=============================================
Problem Setting
---------------
Design and optimize a high-performance parallel N-body simulation. In physics and astronomy, an N-body simulation models the dynamics of particles under gravitational forces. The available hardwar... | tag: hpc
runtime:
language: cpp
timeout_seconds: 600
environment: "C++17 with OpenMP (GCC with libgomp1) on Ubuntu 22.04, 16 vCPUs"
docker:
image: "gcc:13"
resources:
cloud: aws
instance_type: c7i.4xlarge
cpus: "16"
memory: "32"
|
poc_generation/heap_buffer_overflow | research | {"tag": "security"}
| |
poc_generation/heap_use_after_free | research | tag: security
{
"dependencies": {
"uv_project": "resources"
},
"datasets": [
"arvo:47101"
],
"tag": "security"
}
| |
poc_generation/stack_buffer_overflow | research | {"tag": "security"}
| |
poc_generation/uninitialized_value | research | {"tag": "security"}
| |
qknorm | research | QKNorm Optimization Problem
============================
Problem Setting
---------------
Design and optimize high-performance implementations for Query-Key Normalization (QKNorm) on GPU. This problem focuses on implementing efficient normalization kernels that apply RMSNorm to query and key tensors.
This is a **memor... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"runtime": {
"resources": {
"accelerators": "L4:1"
},
"docker": {
"image": "andylizf/triton-tlx:tlx-nv-cu122-nvcc",
"gpu": true
},
"environment": "CUDA 12.2, Python 3.11, PyTorch 2.0+, flashinfer 0.5.0, Tr... |
quant_dot_int4 | research | Quantized Dot (Int4 Packed) Optimization Problem
================================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for a **quantized matrix multiplication** where the left-hand matrix is stored as **packed int4 weights** plus per-group scale/offset, an... | dependencies:
uv_project: resources
datasets: []
tag: hpc
runtime:
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
|
ragged_attention | research | Ragged Attention Optimization Problem
======================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for ragged attention computation on GPU. This problem focuses on implementing efficient kernels that handle variable-length sequences using ragged attention, ... | dependencies:
uv_project: resources
tag: hpc
runtime:
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
resources:
accelerators: L4:1
|
symbolic_regression/mccormick | research | Symbolic Regression Benchmark - McCormick Dataset
=================================================
Problem Setting
---------------
Learn a closed-form symbolic expression `f(x1, x2)` that predicts the target `y`.
This dataset is derived from the McCormick function, a classic 2D optimization test function featuring a... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"tag": "pl"
}
|
symbolic_regression/mixed_polyexp_4d | research | Symbolic Regression Benchmark - Mixed PolyExp 4D Dataset
=========================================================
Problem Setting
---------------
Learn a closed-form symbolic expression `f(x1, x2, x3, x4)` that predicts the target `y`.
This is a higher-dimensional dataset (4 input features) combining polynomial inte... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"tag": "pl"
}
|
symbolic_regression/peaks | research | Symbolic Regression Benchmark - Peaks Dataset
==============================================
Problem Setting
---------------
Learn a closed-form symbolic expression `f(x1, x2)` that predicts the target `y`.
This dataset is based on a peaks-like function, characterized by exponential terms that create localized peaks ... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"tag": "pl"
}
|
symbolic_regression/ripple | research | Symbolic Regression Benchmark - Ripple Dataset
===============================================
Problem Setting
---------------
Learn a closed-form symbolic expression `f(x1, x2)` that predicts the target `y`.
This dataset is generated from a ripple-like function that combines polynomial amplitude modulation with high... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"tag": "pl"
}
|
symbolic_regression/sincos | research | Symbolic Regression Benchmark - SinCos Dataset
===============================================
Problem Setting
---------------
Learn a closed-form symbolic expression `f(x1, x2)` that predicts the target `y`.
This dataset features a function built from basic trigonometric operations. The target exhibits periodic beha... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [],
"tag": "pl"
}
|
vdb_pareto/balanced | research | VDB Design Problem - Balanced Tier
===================================
Problem Setting
---------------
Design a Vector Database index optimized for **recall** subject to a **latency constraint**. This tier uses latency-gated scoring: solutions exceeding the latency threshold receive zero points, while solutions meetin... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [
{
"type": "local_tar",
"path": "resources/sift.tar.gz",
"target": "data/sift1M",
"expected_glob": "*.fvecs"
}
],
"runtime": {
"timeout_seconds": 3600
},
"tag": "db"
}
|
vdb_pareto/high_recall | research | VDB Design Problem - High Recall Tier
======================================
Problem Setting
---------------
Design a Vector Database index optimized for **recall** subject to a **relaxed latency constraint**. This tier uses latency-gated scoring: solutions exceeding the latency threshold receive zero points, while so... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [
{
"type": "local_tar",
"path": "resources/sift.tar.gz",
"target": "data/sift1M",
"expected_glob": "*.fvecs"
}
],
"runtime": {
"timeout_seconds": 3600
},
"tag": "db"
}
|
vdb_pareto/low_latency | research | VDB Design Problem - Low Latency Tier
======================================
Problem Setting
---------------
Design a Vector Database index optimized for **recall** subject to a **strict latency constraint**. This tier uses latency-gated scoring: solutions exceeding the latency threshold receive zero points, while sol... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [
{
"type": "local_tar",
"path": "resources/sift.tar.gz",
"target": "data/sift1M",
"expected_glob": "*.fvecs"
}
],
"runtime": {
"timeout_seconds": 3600
},
"tag": "db"
}
|
vdb_pareto/recall80_latency | research | VDB Design Problem - Recall80 Latency Tier
===========================================
Problem Setting
---------------
Design a Vector Database index optimized for **latency** subject to a **recall constraint**. This tier uses recall-gated scoring: solutions failing to meet the recall threshold receive zero points, wh... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [
{
"type": "local_tar",
"path": "resources/sift.tar.gz",
"target": "data/sift1M",
"expected_glob": "*.fvecs"
}
],
"runtime": {
"timeout_seconds": 3600
},
"tag": "db"
}
|
vdb_pareto/recall95_latency | research | VDB Design Problem - Recall95 Latency Tier
===========================================
Problem Setting
---------------
Design a Vector Database index optimized for **latency** subject to a **high recall constraint**. This tier uses recall-gated scoring: solutions failing to meet the recall threshold receive zero point... | {
"dependencies": {
"uv_project": "resources"
},
"datasets": [
{
"type": "local_tar",
"path": "resources/sift.tar.gz",
"target": "data/sift1M",
"expected_glob": "*.fvecs"
}
],
"runtime": {
"timeout_seconds": 3600
},
"tag": "db"
}
|
vector_addition/2_20 | research | Vector Addition Problem - Medium Vectors (2^20)
================================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for vector addition on GPU with medium vectors (1,048,576 elements). This problem focuses on implementing efficient element-wise addition ... | dependencies:
uv_project: resources
tag: hpc
runtime:
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
|
vector_addition/2_24 | research | Vector Addition Problem - Large Vectors (2^24)
===============================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for vector addition on GPU with large vectors (16,777,216 elements). This problem focuses on implementing efficient element-wise addition fo... | dependencies:
uv_project: resources
datasets: []
tag: hpc
runtime:
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
|
vector_addition/2_28 | research | Vector Addition Problem - Very Large Vectors (2^28)
==============================================
Problem Setting
---------------
Design and optimize high-performance Triton kernels for vector addition on GPU with very large vectors (268,435,456 elements). This problem focuses on implementing efficient element-wise a... | dependencies:
uv_project: resources
datasets: []
tag: hpc
runtime:
docker:
image: andylizf/triton-tlx:tlx-nv-cu122
gpu: true
environment: "Triton 3.2.0 with CUDA 12.2 (triton-tlx image)"
|
bboplace_direct_iccad2015 | 2.0 | BBOPlace Direct ICCAD2015
========================
Problem
-------
Directly submit one JSON macro-placement vector for one visible ICCAD2015
design: `superblue1`. The hidden judge evaluates your vector with the original
BBOPlace-Bench Mask-Guided Optimization (MGO) MP-HPWL evaluator.
This is a VLSI chip-placement op... | tag: optimization
runtime:
language: json
timeout_seconds: 10800
environment: "JSON placement for one visible ICCAD2015 BBOPlace design; hidden evaluator"
apt_packages:
- python3-numpy
docker:
image: ubuntu:24.04
judge_image: ghcr.io/frontiercs/frontiercs-bboplace-data:2026-06-ispd-iccad
visible... |
bboplace_direct_ispd2005 | 2.0 | BBOPlace Direct ISPD2005
=======================
Problem
-------
Directly submit one JSON macro-placement vector for one visible ISPD2005
design: `adaptec1`. The hidden judge evaluates your vector with the original
BBOPlace-Bench Mask-Guided Optimization (MGO) MP-HPWL evaluator.
This is a VLSI chip-placement optimiz... | tag: optimization
runtime:
language: json
timeout_seconds: 10800
environment: "JSON placement for one visible ISPD2005 BBOPlace design; hidden evaluator"
apt_packages:
- python3-numpy
docker:
image: ubuntu:24.04
judge_image: ghcr.io/frontiercs/frontiercs-bboplace-data:2026-06-ispd-iccad
visible_... |
bboplace_iccad2015 | 2.0 | BBOPlace ICCAD2015
==================
Problem
-------
Write a Python solution that proposes macro placements for the BBOPlace
Mask-Guided Optimization (MGO) formulation on eight ICCAD2015 placement
benchmarks:
```text
superblue1, superblue3, superblue4, superblue5,
superblue7, superblue10, superblue16, superblue18
`... | tag: optimization
runtime:
language: python
timeout_seconds: 10800
environment: "Python solution returning BBOPlace MGO placement candidates; hidden ICCAD2015 judge data"
apt_packages:
- python3-numpy
docker:
image: ubuntu:24.04
judge_image: ghcr.io/frontiercs/frontiercs-bboplace-data:2026-06-ispd... |
bboplace_ispd2005 | 2.0 | BBOPlace ISPD2005
=================
Problem
-------
Write a Python solution that proposes macro placements for the BBOPlace
Mask-Guided Optimization (MGO) formulation on six ISPD2005 placement
benchmarks:
```text
adaptec1, adaptec2, adaptec3, adaptec4, bigblue1, bigblue3
```
This is a VLSI chip-placement optimizati... | tag: optimization
runtime:
language: python
timeout_seconds: 10800
environment: "Python solution returning BBOPlace MGO placement candidates; hidden ISPD2005 judge data"
apt_packages:
- python3-numpy
- python3-matplotlib
- python3-pil
docker:
image: ubuntu:24.04
judge_image: ghcr.io/fronti... |
duckdb_e2e_query_optimization | 2.0 | # DuckDB E2E Query Optimization
## Problem
This is an experimental systems task. You are given a pinned DuckDB checkout in
the Harbor workspace and may modify DuckDB itself. Your goal is to improve
end-to-end TPC-H style analytical query performance while preserving the
correctness and generality of DuckDB's SQL exec... | tag: systems
runtime:
language: cpp
timeout_seconds: 10800
environment: "DuckDB source patch; TPC-H shell timing; experimental judge"
apt_packages:
- bash
- build-essential
- ca-certificates
- ccache
- cmake
- git
- ninja-build
- pkg-config
- python3
judge_apt_packages:
... |
erdos_demo | 2.0 | # Erdos Unit Distance Demo
## Problem
Place exactly `N = 10` distinct points in the Euclidean plane so that the
number of point pairs at Euclidean distance exactly `1` is as large as possible.
This is a tiny, visually inspectable demo version of the planar unit distance
problem. If your construction naturally has a ... | tag: geometry
runtime:
language: python
timeout_seconds: 300
environment: "Python 3.11; no external packages required"
docker:
image: ubuntu:24.04
|
erdos_unit_distance | 2.0 | # Erdos Unit Distance
## Problem
Place exactly `N = 65536` distinct points in the Euclidean plane so that the
number of point pairs at Euclidean distance exactly `1` is as large as possible.
This is a finite, executable version of the planar unit distance problem:
given `n` points, maximize the number of pairs at di... | tag: geometry
runtime:
language: python
timeout_seconds: 10800
environment: "Python 3.11; no external packages required"
docker:
image: ubuntu:24.04
|
generals_io_bot | 2.0 | # Generals.io Bot Arena
Image credit: `strakam/generals-bots`, MIT license.
## Problem
Implement a bot for a local Generals.io-style arena. Your bot plays repeated
two-player games against... | tag: games
runtime:
language: patch
timeout_seconds: 10800
environment: "Generals.io bot patch; local generals-bots simulator arena"
apt_packages:
- bash
- ca-certificates
- git
- python3
- python3-pip
judge_apt_packages:
- bash
- ca-certificates
- git
- python3
- pytho... |
vector_db_ann | 2.0 | # Vector DB ANN
## Problem
Build a fast approximate nearest-neighbor vector search engine for a
SIFT1M-scale benchmark.
The hidden benchmark contains exactly `1,000,000` base vectors with dimension
`128`. Queries use the same dimension, distance is squared Euclidean distance,
and each query asks for the top `10` nea... | tag: systems
runtime:
language: rust
timeout_seconds: 10800
environment: "Rust project; hidden ANN benchmark; Python/NumPy judge"
apt_packages:
- build-essential
- cargo
- git
- rustc
judge_apt_packages:
- build-essential
- cargo
- rustc
- python3-pip
- python3-numpy
judg... |
vector_db_ann_disk | 2.0 | # Vector DB ANN Disk
## Problem
Build a fast approximate nearest-neighbor vector search engine for a
SIFT100M-scale benchmark.
The hidden benchmark contains exactly `100,000,000` base vectors with dimension
`128`. Queries use the same dimension, distance is squared Euclidean distance,
and each query asks for the top... | tag: systems
runtime:
language: rust
timeout_seconds: 10800
environment: "Rust project; hidden disk ANN benchmark; Python/NumPy judge"
apt_packages:
- build-essential
- cargo
- git
- rustc
judge_apt_packages:
- build-essential
- cargo
- rustc
- python3-pip
- python3-numpy
... |
vector_db_ann_relaxed | 2.0 | # Vector DB ANN Relaxed
## Problem
Build a fast approximate nearest-neighbor vector search engine for a
SIFT1M-scale benchmark. This relaxed variant uses the same data, API, resource
budget, and recall target as Vector DB ANN, but penalizes load/index-build time
10x less heavily.
The hidden benchmark contains exactl... | tag: systems
runtime:
language: rust
timeout_seconds: 10800
environment: "Rust project; hidden ANN benchmark; Python/NumPy judge"
apt_packages:
- build-essential
- cargo
- git
- rustc
judge_apt_packages:
- build-essential
- cargo
- rustc
- python3-pip
- python3-numpy
judg... |
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