Just-In-Time GPU Compilation for Interpreted Languages with Partial Evaluation

Computer systems are increasingly featuring powerful parallel devices with the advent of many-core CPUs and GPUs. This offers the opportunity to solve computationally-intensive problems at a fraction of the time traditional CPUs need. However, exploiting heterogeneous hardware requires the use of low-level programming language approaches such as OpenCL, which is incredibly challenging, even for advanced programmers. On the application side, interpreted dynamic languages are increasingly becoming popular in many domains due to their simplicity, expressiveness and flexibility. However, this creates a wide gap between the high-level abstractions offered to programmers and the low-level hardware-specific interface. Currently, programmers must rely on high performance libraries or they are forced to write parts of their application in a low-level language like OpenCL. Ideally, non-expert programmers should be able to exploit heterogeneous hardware directly from their interpreted dynamic languages. In this paper, we present a technique to transparently and automatically offload computations from interpreted dynamic languages to heterogeneous devices. Using just-in-time compilation, we automatically generate OpenCL code at runtime which is specialized to the actual observed data types using profiling information. We demonstrate our technique using R, which is a popular interpreted dynamic language predominately used in big data analytic. Our experimental results show the execution on a GPU yields speedups of over 150x compared to the sequential FastR implementation and the obtained performance is competitive with manually written GPU code. We also show that when taking into account start-up time, large speedups are achievable, even when the applications run for as little as a few seconds

Large scale stochastic inventory routing problems with split delivery and service level constraints

A stochastic inventory routing problem (SIRP) is typically the combination of stochastic inventory control problems and NP-hard vehicle routing problems, which determines delivery volumes to the customers that the depot serves in each period, and vehicle routes to deliver the volumes. This paper aims to solve a large scale multi-period SIRP with split delivery (SIRPSD) where a customer’s delivery in each period can be split and satisfied by multiple vehicle routes if necessary. This paper considers SIRPSD under the multi-criteria of the total inventory and transportation costs, and the service levels of customers. The total inventory and transportation cost is considered as the objective of the problem to minimize, while the service levels of the warehouses and the customers are satisfied by some imposed constraints and can be adjusted according to practical requests. In order to tackle the SIRPSD with notorious computational complexity, we first propose an approximate model, which significantly reduces the number of decision variables compared to its corresponding exact model. We then develop a hybrid approach that combines the linearization of nonlinear constraints, the decomposition of the model into sub-models with Lagrangian relaxation, and a partial linearization approach for a sub model. A near optimal solution of the model found by the approach is used to construct a near optimal solution of the SIRPSD. Randomly generated instances of the problem with up to 200 customers and 5 periods and about 400 thousands decision variables where half of them are integer are examined by numerical experiments. Our approach can obtain high quality near optimal solutions within a reasonable amount of computation time on an ordinary PC

Lagrangian relaxation bounds for a production-inventory-routing problem

We consider a single item Production-Inventory-Routing problem with a single producer/supplier and multiple retailers. Inventory management constraints are considered both at the producer and at the retailers, following a vendor managed inventory approach, where the supplier monitors the inventory at retailers and decides on the replenishment policy for each retailer. We assume a constant production capacity. Based on the mathematical formulation we discuss a classical Lagrangian relaxation which allows to decompose the problem into four subproblems, and a new Lagrangian decomposition which decomposes the problem into just a production-inventory subproblem and a routing subproblem. The new decomposition is enhanced with valid inequalities. A computational study is reported to compare the bounds from the two approaches

Revisión con aguja tras implante de válvula de Ahmed en la ciclitis heterocrómica de Fuchs

La ciclitis heterocrómica de Fuchs es una uveítis crónica que puede ser asintomática por años o expresar solo la heterocromía antes que aparezca cualquier otro signo. El glaucoma se considera una de las complicaciones más difíciles de tratar, y requiere cirugía en múltiples ocasiones. Los dispositivos de drenaje están siendo cada vez más utilizados como alternativa de tratamiento quirúrgico en estos casos. Asiste a la consulta médica una paciente de 36 años de edad, con antecedentes de uveítis crónica unilateral del ojo izquierdo asociado a catarata y glaucoma descompensado, a pesar del tratamiento médico. Se presenta con 50 VAR de visión y presión intraocular de 32 mmHg. Se realizó cirugía combinada: facoemulsificación e implante de válvula Ahmed modelo S2 con mitomicina C (0,2 mg/mL) durante cinco minutos. Se diagnostica ampolla de filtración encapsulada en la octava semana. Se realiza revisión con aguja y subconjuntival de 1 mg de bevacizumab (avastin) subtenoniano en área de la filtrante. La inyección se repite días alternos hasta completar tres dosis según protocolo institucional. Se logran cifras de presión intraocular de 17 mmHg y agudeza visual mejor corregida de 95 VAR a los 18 meses posoperatorios